Merge branch 'dev' into extra-networks-always-visible

.github/workflows/run_tests.yaml

@@ -41,6 +41,7 @@ jobs:
           --skip-prepare-environment
           --skip-torch-cuda-test
           --test-server
+          --do-not-download-clip
           --no-half
           --disable-opt-split-attention
           --use-cpu all

CHANGELOG.md

@@ -1,3 +1,93 @@
+## 1.5.1
+
+### Minor:
+ * support parsing text encoder blocks in some new LoRAs
+ * delete scale checker script due to user demand
+
+### Extensions and API:
+ * add postprocess_batch_list script callback
+
+### Bug Fixes:
+ * fix TI training for SD1
+ * fix reload altclip model error
+ * prepend the pythonpath instead of overriding it
+ * fix typo in SD_WEBUI_RESTARTING
+ * if txt2img/img2img raises an exception, finally call state.end()
+ * fix composable diffusion weight parsing
+ * restyle Startup profile for black users
+ * fix webui not launching with --nowebui
+ * catch exception for non git extensions
+ * fix some options missing from /sdapi/v1/options
+ * fix for extension update status always saying "unknown"
+ * fix display of extra network cards that have `<>` in the name
+ * update lora extension to work with python 3.8
+
+
+## 1.5.0
+
+### Features:
+ * SD XL support
+ * user metadata system for custom networks
+ * extended Lora metadata editor: set activation text, default weight, view tags, training info
+ * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
+ * show github stars for extenstions
+ * img2img batch mode can read extra stuff from png info
+ * img2img batch works with subdirectories
+ * hotkeys to move prompt elements: alt+left/right
+ * restyle time taken/VRAM display
+ * add textual inversion hashes to infotext
+ * optimization: cache git extension repo information
+ * move generate button next to the generated picture for mobile clients
+ * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
+ * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
+
+### Minor:
+ * checkbox to check/uncheck all extensions in the Installed tab
+ * add gradio user to infotext and to filename patterns
+ * allow gif for extra network previews
+ * add options to change colors in grid
+ * use natural sort for items in extra networks
+ * Mac: use empty_cache() from torch 2 to clear VRAM
+ * added automatic support for installing the right libraries for Navi3 (AMD)
+ * add option SWIN_torch_compile to accelerate SwinIR upscale
+ * suppress printing TI embedding info at start to console by default
+ * speedup extra networks listing
+ * added `[none]` filename token.
+ * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
+ * add always_discard_next_to_last_sigma option to XYZ plot
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
+ 
+### Extensions and API:
+ * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
+ * allow Script to have custom metaclass
+ * add model exists status check /sdapi/v1/options
+ * rename --add-stop-route to --api-server-stop
+ * add `before_hr` script callback
+ * add callback `after_extra_networks_activate`
+ * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
+ * return http 404 when thumb file not found
+ * allow replacing extensions index with environment variable
+ 
+### Bug Fixes:
+ * fix for catch errors when retrieving extension index #11290
+ * fix very slow loading speed of .safetensors files when reading from network drives
+ * API cache cleanup
+ * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
+ * fix warning of 'has_mps' deprecated from PyTorch
+ * fix problem with extra network saving images as previews losing generation info
+ * fix throwing exception when trying to resize image with I;16 mode
+ * fix for #11534: canvas zoom and pan extension hijacking shortcut keys
+ * fixed launch script to be runnable from any directory
+ * don't add "Seed Resize: -1x-1" to API image metadata
+ * correctly remove end parenthesis with ctrl+up/down
+ * fixing --subpath on newer gradio version
+ * fix: check fill size none zero when resize  (fixes #11425)
+ * use submit and blur for quick settings textbox
+ * save img2img batch with images.save_image()
+ * prevent running preload.py for disabled extensions
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
+
+
 ## 1.4.1
 
 ### Bug Fixes:

README.md

@@ -88,7 +88,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
 - Now without any bad letters!
 - Load checkpoints in safetensors format
-- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
+- Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
 
@@ -168,5 +168,7 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Security advice - RyotaK
 - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
+- LyCORIS - KohakuBlueleaf
+- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

extensions-builtin/Lora/extra_networks_lora.py

@@ -1,5 +1,5 @@
 from modules import extra_networks, shared
-import lora
+import networks
 
 
 class ExtraNetworkLora(extra_networks.ExtraNetwork):
@@ -9,24 +9,38 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
-        if additional != "None" and additional in lora.available_loras and not any(x for x in params_list if x.items[0] == additional):
+        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]))
 
         names = []
-        multipliers = []
+        te_multipliers = []
+        unet_multipliers = []
+        dyn_dims = []
         for params in params_list:
             assert params.items
 
-            names.append(params.items[0])
-            multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
+            names.append(params.positional[0])
 
-        lora.load_loras(names, multipliers)
+            te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
+            te_multiplier = float(params.named.get("te", te_multiplier))
+
+            unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
+            unet_multiplier = float(params.named.get("unet", unet_multiplier))
+
+            dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
+            dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
+
+            te_multipliers.append(te_multiplier)
+            unet_multipliers.append(unet_multiplier)
+            dyn_dims.append(dyn_dim)
+
+        networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
 
         if shared.opts.lora_add_hashes_to_infotext:
-            lora_hashes = []
-            for item in lora.loaded_loras:
-                shorthash = item.lora_on_disk.shorthash
+            network_hashes = []
+            for item in networks.loaded_networks:
+                shorthash = item.network_on_disk.shorthash
                 if not shorthash:
                     continue
 
@@ -36,10 +50,10 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
 
                 alias = alias.replace(":", "").replace(",", "")
 
-                lora_hashes.append(f"{alias}: {shorthash}")
+                network_hashes.append(f"{alias}: {shorthash}")
 
-            if lora_hashes:
-                p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes)
+            if network_hashes:
+                p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
 
     def deactivate(self, p):
         pass

extensions-builtin/Lora/lora.py

@@ -1,511 +1,9 @@
-import os
-import re
-import torch
-from typing import Union
+import networks
 
-from modules import shared, devices, sd_models, errors, scripts, sd_hijack, hashes, cache
+list_available_loras = networks.list_available_networks
 
-metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
-
-re_digits = re.compile(r"\d+")
-re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
-re_compiled = {}
-
-suffix_conversion = {
-    "attentions": {},
-    "resnets": {
-        "conv1": "in_layers_2",
-        "conv2": "out_layers_3",
-        "time_emb_proj": "emb_layers_1",
-        "conv_shortcut": "skip_connection",
-    }
-}
-
-
-def convert_diffusers_name_to_compvis(key, is_sd2):
-    def match(match_list, regex_text):
-        regex = re_compiled.get(regex_text)
-        if regex is None:
-            regex = re.compile(regex_text)
-            re_compiled[regex_text] = regex
-
-        r = re.match(regex, key)
-        if not r:
-            return False
-
-        match_list.clear()
-        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
-        return True
-
-    m = []
-
-    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
-        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
-        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
-
-    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
-        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
-        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
-
-    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
-        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
-        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
-
-    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
-        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
-
-    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
-        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
-
-    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
-        if is_sd2:
-            if 'mlp_fc1' in m[1]:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
-            elif 'mlp_fc2' in m[1]:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
-            else:
-                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
-
-        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
-
-    return key
-
-
-class LoraOnDisk:
-    def __init__(self, name, filename):
-        self.name = name
-        self.filename = filename
-        self.metadata = {}
-        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
-
-        def read_metadata():
-            metadata = sd_models.read_metadata_from_safetensors(filename)
-            metadata.pop('ssmd_cover_images', None)  # those are cover images, and they are too big to display in UI as text
-
-            return metadata
-
-        if self.is_safetensors:
-            try:
-                self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
-            except Exception as e:
-                errors.display(e, f"reading lora {filename}")
-
-        if self.metadata:
-            m = {}
-            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
-                m[k] = v
-
-            self.metadata = m
-
-        self.alias = self.metadata.get('ss_output_name', self.name)
-
-        self.hash = None
-        self.shorthash = None
-        self.set_hash(
-            self.metadata.get('sshs_model_hash') or
-            hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
-            ''
-        )
-
-    def set_hash(self, v):
-        self.hash = v
-        self.shorthash = self.hash[0:12]
-
-        if self.shorthash:
-            available_lora_hash_lookup[self.shorthash] = self
-
-    def read_hash(self):
-        if not self.hash:
-            self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
-
-    def get_alias(self):
-        if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in forbidden_lora_aliases:
-            return self.name
-        else:
-            return self.alias
-
-
-class LoraModule:
-    def __init__(self, name, lora_on_disk: LoraOnDisk):
-        self.name = name
-        self.lora_on_disk = lora_on_disk
-        self.multiplier = 1.0
-        self.modules = {}
-        self.mtime = None
-
-        self.mentioned_name = None
-        """the text that was used to add lora to prompt - can be either name or an alias"""
-
-
-class LoraUpDownModule:
-    def __init__(self):
-        self.up = None
-        self.down = None
-        self.alpha = None
-
-
-def assign_lora_names_to_compvis_modules(sd_model):
-    lora_layer_mapping = {}
-
-    for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
-        lora_name = name.replace(".", "_")
-        lora_layer_mapping[lora_name] = module
-        module.lora_layer_name = lora_name
-
-    for name, module in shared.sd_model.model.named_modules():
-        lora_name = name.replace(".", "_")
-        lora_layer_mapping[lora_name] = module
-        module.lora_layer_name = lora_name
-
-    sd_model.lora_layer_mapping = lora_layer_mapping
-
-
-def load_lora(name, lora_on_disk):
-    lora = LoraModule(name, lora_on_disk)
-    lora.mtime = os.path.getmtime(lora_on_disk.filename)
-
-    sd = sd_models.read_state_dict(lora_on_disk.filename)
-
-    # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
-    if not hasattr(shared.sd_model, 'lora_layer_mapping'):
-        assign_lora_names_to_compvis_modules(shared.sd_model)
-
-    keys_failed_to_match = {}
-    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
-
-    for key_diffusers, weight in sd.items():
-        key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
-        key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
-
-        sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
-
-        if sd_module is None:
-            m = re_x_proj.match(key)
-            if m:
-                sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
-
-        if sd_module is None:
-            keys_failed_to_match[key_diffusers] = key
-            continue
-
-        lora_module = lora.modules.get(key, None)
-        if lora_module is None:
-            lora_module = LoraUpDownModule()
-            lora.modules[key] = lora_module
-
-        if lora_key == "alpha":
-            lora_module.alpha = weight.item()
-            continue
-
-        if type(sd_module) == torch.nn.Linear:
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.MultiheadAttention:
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
-            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
-        elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
-            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
-        else:
-            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
-            continue
-            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
-
-        with torch.no_grad():
-            module.weight.copy_(weight)
-
-        module.to(device=devices.cpu, dtype=devices.dtype)
-
-        if lora_key == "lora_up.weight":
-            lora_module.up = module
-        elif lora_key == "lora_down.weight":
-            lora_module.down = module
-        else:
-            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
-
-    if keys_failed_to_match:
-        print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")
-
-    return lora
-
-
-def load_loras(names, multipliers=None):
-    already_loaded = {}
-
-    for lora in loaded_loras:
-        if lora.name in names:
-            already_loaded[lora.name] = lora
-
-    loaded_loras.clear()
-
-    loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
-    if any(x is None for x in loras_on_disk):
-        list_available_loras()
-
-        loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
-
-    failed_to_load_loras = []
-
-    for i, name in enumerate(names):
-        lora = already_loaded.get(name, None)
-
-        lora_on_disk = loras_on_disk[i]
-
-        if lora_on_disk is not None:
-            if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
-                try:
-                    lora = load_lora(name, lora_on_disk)
-                except Exception as e:
-                    errors.display(e, f"loading Lora {lora_on_disk.filename}")
-                    continue
-
-            lora.mentioned_name = name
-
-            lora_on_disk.read_hash()
-
-        if lora is None:
-            failed_to_load_loras.append(name)
-            print(f"Couldn't find Lora with name {name}")
-            continue
-
-        lora.multiplier = multipliers[i] if multipliers else 1.0
-        loaded_loras.append(lora)
-
-    if failed_to_load_loras:
-        sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras))
-
-
-def lora_calc_updown(lora, module, target):
-    with torch.no_grad():
-        up = module.up.weight.to(target.device, dtype=target.dtype)
-        down = module.down.weight.to(target.device, dtype=target.dtype)
-
-        if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
-            updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
-        elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
-            updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
-        else:
-            updown = up @ down
-
-        updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
-
-        return updown
-
-
-def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
-    weights_backup = getattr(self, "lora_weights_backup", None)
-
-    if weights_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])
-    else:
-        self.weight.copy_(weights_backup)
-
-
-def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
-    """
-    Applies the currently selected set of Loras to the weights of torch layer self.
-    If weights already have this particular set of loras applied, does nothing.
-    If not, restores orginal weights from backup and alters weights according to loras.
-    """
-
-    lora_layer_name = getattr(self, 'lora_layer_name', None)
-    if lora_layer_name is None:
-        return
-
-    current_names = getattr(self, "lora_current_names", ())
-    wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
-
-    weights_backup = getattr(self, "lora_weights_backup", None)
-    if weights_backup is None:
-        if isinstance(self, torch.nn.MultiheadAttention):
-            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
-        else:
-            weights_backup = self.weight.to(devices.cpu, copy=True)
-
-        self.lora_weights_backup = weights_backup
-
-    if current_names != wanted_names:
-        lora_restore_weights_from_backup(self)
-
-        for lora in loaded_loras:
-            module = lora.modules.get(lora_layer_name, None)
-            if module is not None and hasattr(self, 'weight'):
-                self.weight += lora_calc_updown(lora, module, self.weight)
-                continue
-
-            module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
-            module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
-            module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
-            module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
-
-            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
-                updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
-                updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
-                updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-
-                self.in_proj_weight += updown_qkv
-                self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
-                continue
-
-            if module is None:
-                continue
-
-            print(f'failed to calculate lora weights for layer {lora_layer_name}')
-
-        self.lora_current_names = wanted_names
-
-
-def lora_forward(module, input, original_forward):
-    """
-    Old way of applying Lora by executing operations during layer's forward.
-    Stacking many loras this way results in big performance degradation.
-    """
-
-    if len(loaded_loras) == 0:
-        return original_forward(module, input)
-
-    input = devices.cond_cast_unet(input)
-
-    lora_restore_weights_from_backup(module)
-    lora_reset_cached_weight(module)
-
-    res = original_forward(module, input)
-
-    lora_layer_name = getattr(module, 'lora_layer_name', None)
-    for lora in loaded_loras:
-        module = lora.modules.get(lora_layer_name, None)
-        if module is None:
-            continue
-
-        module.up.to(device=devices.device)
-        module.down.to(device=devices.device)
-
-        res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
-
-    return res
-
-
-def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    self.lora_current_names = ()
-    self.lora_weights_backup = None
-
-
-def lora_Linear_forward(self, input):
-    if shared.opts.lora_functional:
-        return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
-
-    lora_apply_weights(self)
-
-    return torch.nn.Linear_forward_before_lora(self, input)
-
-
-def lora_Linear_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
-
-
-def lora_Conv2d_forward(self, input):
-    if shared.opts.lora_functional:
-        return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
-
-    lora_apply_weights(self)
-
-    return torch.nn.Conv2d_forward_before_lora(self, input)
-
-
-def lora_Conv2d_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
-
-
-def lora_MultiheadAttention_forward(self, *args, **kwargs):
-    lora_apply_weights(self)
-
-    return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
-
-
-def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
-    lora_reset_cached_weight(self)
-
-    return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
-
-
-def list_available_loras():
-    available_loras.clear()
-    available_lora_aliases.clear()
-    forbidden_lora_aliases.clear()
-    available_lora_hash_lookup.clear()
-    forbidden_lora_aliases.update({"none": 1, "Addams": 1})
-
-    os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
-
-    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
-    for filename in candidates:
-        if os.path.isdir(filename):
-            continue
-
-        name = os.path.splitext(os.path.basename(filename))[0]
-        try:
-            entry = LoraOnDisk(name, filename)
-        except OSError:  # should catch FileNotFoundError and PermissionError etc.
-            errors.report(f"Failed to load LoRA {name} from {filename}", exc_info=True)
-            continue
-
-        available_loras[name] = entry
-
-        if entry.alias in available_lora_aliases:
-            forbidden_lora_aliases[entry.alias.lower()] = 1
-
-        available_lora_aliases[name] = entry
-        available_lora_aliases[entry.alias] = entry
-
-
-re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
-
-
-def infotext_pasted(infotext, params):
-    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
-        return  # if the other extension is active, it will handle those fields, no need to do anything
-
-    added = []
-
-    for k in params:
-        if not k.startswith("AddNet Model "):
-            continue
-
-        num = k[13:]
-
-        if params.get("AddNet Module " + num) != "LoRA":
-            continue
-
-        name = params.get("AddNet Model " + num)
-        if name is None:
-            continue
-
-        m = re_lora_name.match(name)
-        if m:
-            name = m.group(1)
-
-        multiplier = params.get("AddNet Weight A " + num, "1.0")
-
-        added.append(f"<lora:{name}:{multiplier}>")
-
-    if added:
-        params["Prompt"] += "\n" + "".join(added)
-
-
-available_loras = {}
-available_lora_aliases = {}
-available_lora_hash_lookup = {}
-forbidden_lora_aliases = {}
-loaded_loras = []
-
-list_available_loras()
+available_loras = networks.available_networks
+available_lora_aliases = networks.available_network_aliases
+available_lora_hash_lookup = networks.available_network_hash_lookup
+forbidden_lora_aliases = networks.forbidden_network_aliases
+loaded_loras = networks.loaded_networks

extensions-builtin/Lora/lyco_helpers.py

@@ -0,0 +1,21 @@
+import torch
+
+
+def make_weight_cp(t, wa, wb):
+    temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
+    return torch.einsum('i j k l, i r -> r j k l', temp, wa)
+
+
+def rebuild_conventional(up, down, shape, dyn_dim=None):
+    up = up.reshape(up.size(0), -1)
+    down = down.reshape(down.size(0), -1)
+    if dyn_dim is not None:
+        up = up[:, :dyn_dim]
+        down = down[:dyn_dim, :]
+    return (up @ down).reshape(shape)
+
+
+def rebuild_cp_decomposition(up, down, mid):
+    up = up.reshape(up.size(0), -1)
+    down = down.reshape(down.size(0), -1)
+    return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)

extensions-builtin/Lora/network.py

@@ -0,0 +1,155 @@
+from __future__ import annotations
+import os
+from collections import namedtuple
+import enum
+
+from modules import sd_models, cache, errors, hashes, shared
+
+NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
+
+
+class SdVersion(enum.Enum):
+    Unknown = 1
+    SD1 = 2
+    SD2 = 3
+    SDXL = 4
+
+
+class NetworkOnDisk:
+    def __init__(self, name, filename):
+        self.name = name
+        self.filename = filename
+        self.metadata = {}
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
+        def read_metadata():
+            metadata = sd_models.read_metadata_from_safetensors(filename)
+            metadata.pop('ssmd_cover_images', None)  # those are cover images, and they are too big to display in UI as text
+
+            return metadata
+
+        if self.is_safetensors:
+            try:
+                self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
+            except Exception as e:
+                errors.display(e, f"reading lora {filename}")
+
+        if self.metadata:
+            m = {}
+            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+                m[k] = v
+
+            self.metadata = m
+
+        self.alias = self.metadata.get('ss_output_name', self.name)
+
+        self.hash = None
+        self.shorthash = None
+        self.set_hash(
+            self.metadata.get('sshs_model_hash') or
+            hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
+            ''
+        )
+
+        self.sd_version = self.detect_version()
+
+    def detect_version(self):
+        if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
+            return SdVersion.SDXL
+        elif str(self.metadata.get('ss_v2', "")) == "True":
+            return SdVersion.SD2
+        elif len(self.metadata):
+            return SdVersion.SD1
+
+        return SdVersion.Unknown
+
+    def set_hash(self, v):
+        self.hash = v
+        self.shorthash = self.hash[0:12]
+
+        if self.shorthash:
+            import networks
+            networks.available_network_hash_lookup[self.shorthash] = self
+
+    def read_hash(self):
+        if not self.hash:
+            self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
+
+    def get_alias(self):
+        import networks
+        if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
+            return self.name
+        else:
+            return self.alias
+
+
+class Network:  # LoraModule
+    def __init__(self, name, network_on_disk: NetworkOnDisk):
+        self.name = name
+        self.network_on_disk = network_on_disk
+        self.te_multiplier = 1.0
+        self.unet_multiplier = 1.0
+        self.dyn_dim = None
+        self.modules = {}
+        self.mtime = None
+
+        self.mentioned_name = None
+        """the text that was used to add the network to prompt - can be either name or an alias"""
+
+
+class ModuleType:
+    def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
+        return None
+
+
+class NetworkModule:
+    def __init__(self, net: Network, weights: NetworkWeights):
+        self.network = net
+        self.network_key = weights.network_key
+        self.sd_key = weights.sd_key
+        self.sd_module = weights.sd_module
+
+        if hasattr(self.sd_module, 'weight'):
+            self.shape = self.sd_module.weight.shape
+
+        self.dim = None
+        self.bias = weights.w.get("bias")
+        self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
+        self.scale = weights.w["scale"].item() if "scale" in weights.w else None
+
+    def multiplier(self):
+        if 'transformer' in self.sd_key[:20]:
+            return self.network.te_multiplier
+        else:
+            return self.network.unet_multiplier
+
+    def calc_scale(self):
+        if self.scale is not None:
+            return self.scale
+        if self.dim is not None and self.alpha is not None:
+            return self.alpha / self.dim
+
+        return 1.0
+
+    def finalize_updown(self, updown, orig_weight, output_shape):
+        if self.bias is not None:
+            updown = updown.reshape(self.bias.shape)
+            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown = updown.reshape(output_shape)
+
+        if len(output_shape) == 4:
+            updown = updown.reshape(output_shape)
+
+        if orig_weight.size().numel() == updown.size().numel():
+            updown = updown.reshape(orig_weight.shape)
+
+        return updown * self.calc_scale() * self.multiplier()
+
+    def calc_updown(self, target):
+        raise NotImplementedError()
+
+    def forward(self, x, y):
+        raise NotImplementedError()
+

extensions-builtin/Lora/network_full.py

@@ -0,0 +1,22 @@
+import network
+
+
+class ModuleTypeFull(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["diff"]):
+            return NetworkModuleFull(net, weights)
+
+        return None
+
+
+class NetworkModuleFull(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.weight = weights.w.get("diff")
+
+    def calc_updown(self, orig_weight):
+        output_shape = self.weight.shape
+        updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_hada.py

@@ -0,0 +1,55 @@
+import lyco_helpers
+import network
+
+
+class ModuleTypeHada(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
+            return NetworkModuleHada(net, weights)
+
+        return None
+
+
+class NetworkModuleHada(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        if hasattr(self.sd_module, 'weight'):
+            self.shape = self.sd_module.weight.shape
+
+        self.w1a = weights.w["hada_w1_a"]
+        self.w1b = weights.w["hada_w1_b"]
+        self.dim = self.w1b.shape[0]
+        self.w2a = weights.w["hada_w2_a"]
+        self.w2b = weights.w["hada_w2_b"]
+
+        self.t1 = weights.w.get("hada_t1")
+        self.t2 = weights.w.get("hada_t2")
+
+    def calc_updown(self, orig_weight):
+        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        output_shape = [w1a.size(0), w1b.size(1)]
+
+        if self.t1 is not None:
+            output_shape = [w1a.size(1), w1b.size(1)]
+            t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
+            output_shape += t1.shape[2:]
+        else:
+            if len(w1b.shape) == 4:
+                output_shape += w1b.shape[2:]
+            updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
+
+        if self.t2 is not None:
+            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+        else:
+            updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
+
+        updown = updown1 * updown2
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_ia3.py

@@ -0,0 +1,30 @@
+import network
+
+
+class ModuleTypeIa3(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["weight"]):
+            return NetworkModuleIa3(net, weights)
+
+        return None
+
+
+class NetworkModuleIa3(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w = weights.w["weight"]
+        self.on_input = weights.w["on_input"].item()
+
+    def calc_updown(self, orig_weight):
+        w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        output_shape = [w.size(0), orig_weight.size(1)]
+        if self.on_input:
+            output_shape.reverse()
+        else:
+            w = w.reshape(-1, 1)
+
+        updown = orig_weight * w
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_lokr.py

@@ -0,0 +1,64 @@
+import torch
+
+import lyco_helpers
+import network
+
+
+class ModuleTypeLokr(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
+        has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
+        if has_1 and has_2:
+            return NetworkModuleLokr(net, weights)
+
+        return None
+
+
+def make_kron(orig_shape, w1, w2):
+    if len(w2.shape) == 4:
+        w1 = w1.unsqueeze(2).unsqueeze(2)
+    w2 = w2.contiguous()
+    return torch.kron(w1, w2).reshape(orig_shape)
+
+
+class NetworkModuleLokr(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w1 = weights.w.get("lokr_w1")
+        self.w1a = weights.w.get("lokr_w1_a")
+        self.w1b = weights.w.get("lokr_w1_b")
+        self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
+        self.w2 = weights.w.get("lokr_w2")
+        self.w2a = weights.w.get("lokr_w2_a")
+        self.w2b = weights.w.get("lokr_w2_b")
+        self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
+        self.t2 = weights.w.get("lokr_t2")
+
+    def calc_updown(self, orig_weight):
+        if self.w1 is not None:
+            w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+        else:
+            w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1 = w1a @ w1b
+
+        if self.w2 is not None:
+            w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+        elif self.t2 is None:
+            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2 = w2a @ w2b
+        else:
+            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+
+        output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
+        if len(orig_weight.shape) == 4:
+            output_shape = orig_weight.shape
+
+        updown = make_kron(output_shape, w1, w2)
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_lora.py

@@ -0,0 +1,86 @@
+import torch
+
+import lyco_helpers
+import network
+from modules import devices
+
+
+class ModuleTypeLora(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
+            return NetworkModuleLora(net, weights)
+
+        return None
+
+
+class NetworkModuleLora(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.up_model = self.create_module(weights.w, "lora_up.weight")
+        self.down_model = self.create_module(weights.w, "lora_down.weight")
+        self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
+
+        self.dim = weights.w["lora_down.weight"].shape[0]
+
+    def create_module(self, weights, key, none_ok=False):
+        weight = weights.get(key)
+
+        if weight is None and none_ok:
+            return None
+
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
+        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
+
+        if is_linear:
+            weight = weight.reshape(weight.shape[0], -1)
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif is_conv and key == "lora_down.weight" or key == "dyn_up":
+            if len(weight.shape) == 2:
+                weight = weight.reshape(weight.shape[0], -1, 1, 1)
+
+            if weight.shape[2] != 1 or weight.shape[3] != 1:
+                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+            else:
+                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        elif is_conv and key == "lora_mid.weight":
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+        elif is_conv and key == "lora_up.weight" or key == "dyn_down":
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        else:
+            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
+
+        with torch.no_grad():
+            if weight.shape != module.weight.shape:
+                weight = weight.reshape(module.weight.shape)
+            module.weight.copy_(weight)
+
+        module.to(device=devices.cpu, dtype=devices.dtype)
+        module.weight.requires_grad_(False)
+
+        return module
+
+    def calc_updown(self, orig_weight):
+        up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        output_shape = [up.size(0), down.size(1)]
+        if self.mid_model is not None:
+            # cp-decomposition
+            mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
+            output_shape += mid.shape[2:]
+        else:
+            if len(down.shape) == 4:
+                output_shape += down.shape[2:]
+            updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
+
+        return self.finalize_updown(updown, orig_weight, output_shape)
+
+    def forward(self, x, y):
+        self.up_model.to(device=devices.device)
+        self.down_model.to(device=devices.device)
+
+        return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
+
+

extensions-builtin/Lora/networks.py

@@ -0,0 +1,468 @@
+import os
+import re
+
+import network
+import network_lora
+import network_hada
+import network_ia3
+import network_lokr
+import network_full
+
+import torch
+from typing import Union
+
+from modules import shared, devices, sd_models, errors, scripts, sd_hijack
+
+module_types = [
+    network_lora.ModuleTypeLora(),
+    network_hada.ModuleTypeHada(),
+    network_ia3.ModuleTypeIa3(),
+    network_lokr.ModuleTypeLokr(),
+    network_full.ModuleTypeFull(),
+]
+
+
+re_digits = re.compile(r"\d+")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+    "attentions": {},
+    "resnets": {
+        "conv1": "in_layers_2",
+        "conv2": "out_layers_3",
+        "time_emb_proj": "emb_layers_1",
+        "conv_shortcut": "skip_connection",
+    }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+    def match(match_list, regex_text):
+        regex = re_compiled.get(regex_text)
+        if regex is None:
+            regex = re.compile(regex_text)
+            re_compiled[regex_text] = regex
+
+        r = re.match(regex, key)
+        if not r:
+            return False
+
+        match_list.clear()
+        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+        return True
+
+    m = []
+
+    if match(m, r"lora_unet_conv_in(.*)"):
+        return f'diffusion_model_input_blocks_0_0{m[0]}'
+
+    if match(m, r"lora_unet_conv_out(.*)"):
+        return f'diffusion_model_out_2{m[0]}'
+
+    if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
+        return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+        if is_sd2:
+            if 'mlp_fc1' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+            elif 'mlp_fc2' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+            else:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+    if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
+        if 'mlp_fc1' in m[1]:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+        elif 'mlp_fc2' in m[1]:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+        else:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+    return key
+
+
+def assign_network_names_to_compvis_modules(sd_model):
+    network_layer_mapping = {}
+
+    if shared.sd_model.is_sdxl:
+        for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
+            if not hasattr(embedder, 'wrapped'):
+                continue
+
+            for name, module in embedder.wrapped.named_modules():
+                network_name = f'{i}_{name.replace(".", "_")}'
+                network_layer_mapping[network_name] = module
+                module.network_layer_name = network_name
+    else:
+        for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+            network_name = name.replace(".", "_")
+            network_layer_mapping[network_name] = module
+            module.network_layer_name = network_name
+
+    for name, module in shared.sd_model.model.named_modules():
+        network_name = name.replace(".", "_")
+        network_layer_mapping[network_name] = module
+        module.network_layer_name = network_name
+
+    sd_model.network_layer_mapping = network_layer_mapping
+
+
+def load_network(name, network_on_disk):
+    net = network.Network(name, network_on_disk)
+    net.mtime = os.path.getmtime(network_on_disk.filename)
+
+    sd = sd_models.read_state_dict(network_on_disk.filename)
+
+    # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
+    if not hasattr(shared.sd_model, 'network_layer_mapping'):
+        assign_network_names_to_compvis_modules(shared.sd_model)
+
+    keys_failed_to_match = {}
+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
+
+    matched_networks = {}
+
+    for key_network, weight in sd.items():
+        key_network_without_network_parts, network_part = key_network.split(".", 1)
+
+        key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
+        sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        if sd_module is None:
+            m = re_x_proj.match(key)
+            if m:
+                sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
+
+        # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
+        if sd_module is None and "lora_unet" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+        elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+            # some SD1 Loras also have correct compvis keys
+            if sd_module is None:
+                key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
+                sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        if sd_module is None:
+            keys_failed_to_match[key_network] = key
+            continue
+
+        if key not in matched_networks:
+            matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
+
+        matched_networks[key].w[network_part] = weight
+
+    for key, weights in matched_networks.items():
+        net_module = None
+        for nettype in module_types:
+            net_module = nettype.create_module(net, weights)
+            if net_module is not None:
+                break
+
+        if net_module is None:
+            raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
+
+        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}")
+
+    return net
+
+
+def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+    already_loaded = {}
+
+    for net in loaded_networks:
+        if net.name in names:
+            already_loaded[net.name] = net
+
+    loaded_networks.clear()
+
+    networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+    if any(x is None for x in networks_on_disk):
+        list_available_networks()
+
+        networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+
+    failed_to_load_networks = []
+
+    for i, name in enumerate(names):
+        net = already_loaded.get(name, None)
+
+        network_on_disk = networks_on_disk[i]
+
+        if network_on_disk is not None:
+            if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
+                try:
+                    net = load_network(name, network_on_disk)
+                except Exception as e:
+                    errors.display(e, f"loading network {network_on_disk.filename}")
+                    continue
+
+            net.mentioned_name = name
+
+            network_on_disk.read_hash()
+
+        if net is None:
+            failed_to_load_networks.append(name)
+            print(f"Couldn't find network with name {name}")
+            continue
+
+        net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
+        net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
+        net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
+        loaded_networks.append(net)
+
+    if failed_to_load_networks:
+        sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+
+
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+    weights_backup = getattr(self, "network_weights_backup", None)
+
+    if weights_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])
+    else:
+        self.weight.copy_(weights_backup)
+
+
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, 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.
+    If not, restores orginal weights from backup and alters weights according to networks.
+    """
+
+    network_layer_name = getattr(self, 'network_layer_name', None)
+    if network_layer_name is None:
+        return
+
+    current_names = getattr(self, "network_current_names", ())
+    wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
+
+    weights_backup = getattr(self, "network_weights_backup", None)
+    if weights_backup is None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+        else:
+            weights_backup = self.weight.to(devices.cpu, copy=True)
+
+        self.network_weights_backup = weights_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)
+
+                    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
+
+            module_q = net.modules.get(network_layer_name + "_q_proj", None)
+            module_k = net.modules.get(network_layer_name + "_k_proj", None)
+            module_v = net.modules.get(network_layer_name + "_v_proj", None)
+            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)
+
+                    self.in_proj_weight += updown_qkv
+                    self.out_proj.weight += updown_out
+                    continue
+
+            if module is None:
+                continue
+
+            print(f'failed to calculate network weights for layer {network_layer_name}')
+
+        self.network_current_names = wanted_names
+
+
+def network_forward(module, input, original_forward):
+    """
+    Old way of applying Lora by executing operations during layer's forward.
+    Stacking many loras this way results in big performance degradation.
+    """
+
+    if len(loaded_networks) == 0:
+        return original_forward(module, input)
+
+    input = devices.cond_cast_unet(input)
+
+    network_restore_weights_from_backup(module)
+    network_reset_cached_weight(module)
+
+    y = original_forward(module, input)
+
+    network_layer_name = getattr(module, 'network_layer_name', None)
+    for lora in loaded_networks:
+        module = lora.modules.get(network_layer_name, None)
+        if module is None:
+            continue
+
+        y = module.forward(y, input)
+
+    return y
+
+
+def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+    self.network_current_names = ()
+    self.network_weights_backup = None
+
+
+def network_Linear_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.Linear_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.Linear_forward_before_network(self, input)
+
+
+def network_Linear_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def network_Conv2d_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.Conv2d_forward_before_network(self, input)
+
+
+def network_Conv2d_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_forward(self, *args, **kwargs):
+    network_apply_weights(self)
+
+    return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def list_available_networks():
+    available_networks.clear()
+    available_network_aliases.clear()
+    forbidden_network_aliases.clear()
+    available_network_hash_lookup.clear()
+    forbidden_network_aliases.update({"none": 1, "Addams": 1})
+
+    os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
+
+    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+    candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+    for filename in candidates:
+        if os.path.isdir(filename):
+            continue
+
+        name = os.path.splitext(os.path.basename(filename))[0]
+        try:
+            entry = network.NetworkOnDisk(name, filename)
+        except OSError:  # should catch FileNotFoundError and PermissionError etc.
+            errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
+            continue
+
+        available_networks[name] = entry
+
+        if entry.alias in available_network_aliases:
+            forbidden_network_aliases[entry.alias.lower()] = 1
+
+        available_network_aliases[name] = entry
+        available_network_aliases[entry.alias] = entry
+
+
+re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+        return  # if the other extension is active, it will handle those fields, no need to do anything
+
+    added = []
+
+    for k in params:
+        if not k.startswith("AddNet Model "):
+            continue
+
+        num = k[13:]
+
+        if params.get("AddNet Module " + num) != "LoRA":
+            continue
+
+        name = params.get("AddNet Model " + num)
+        if name is None:
+            continue
+
+        m = re_network_name.match(name)
+        if m:
+            name = m.group(1)
+
+        multiplier = params.get("AddNet Weight A " + num, "1.0")
+
+        added.append(f"<lora:{name}:{multiplier}>")
+
+    if added:
+        params["Prompt"] += "\n" + "".join(added)
+
+
+available_networks = {}
+available_network_aliases = {}
+loaded_networks = []
+available_network_hash_lookup = {}
+forbidden_network_aliases = {}
+
+list_available_networks()

extensions-builtin/Lora/preload.py

@@ -4,3 +4,4 @@ from modules import paths
 
 def preload(parser):
     parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
+    parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))

extensions-builtin/Lora/scripts/lora_script.py

@@ -4,69 +4,76 @@ import torch
 import gradio as gr
 from fastapi import FastAPI
 
-import lora
+import network
+import networks
+import lora  # noqa:F401
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
 def unload():
-    torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
-    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
-    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
-    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
-    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
-    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
+    torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
+    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
+    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
+    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
+    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
+    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
 
 
 def before_ui():
     ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
-    extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
+
+    extra_network = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(extra_network)
+    extra_networks.register_extra_network_alias(extra_network, "lyco")
 
 
-if not hasattr(torch.nn, 'Linear_forward_before_lora'):
-    torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+if not hasattr(torch.nn, 'Linear_forward_before_network'):
+    torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
 
-if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
-    torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
+    torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
 
-if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
-    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
+    torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
 
-if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
-    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+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, 'MultiheadAttention_forward_before_lora'):
-    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
+    torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
 
-if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
-    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
+    torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
 
-torch.nn.Linear.forward = lora.lora_Linear_forward
-torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
-torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
-torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
-torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
-torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
+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.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
 
-script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
+script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
-script_callbacks.on_infotext_pasted(lora.infotext_pasted)
+script_callbacks.on_infotext_pasted(networks.infotext_pasted)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
+    "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
     "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
     "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
+    "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
+    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
 }))
 
 
 shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
-    "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
+    "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
 }))
 
 
-def create_lora_json(obj: lora.LoraOnDisk):
+def create_lora_json(obj: network.NetworkOnDisk):
     return {
         "name": obj.name,
         "alias": obj.alias,
@@ -75,17 +82,17 @@ def create_lora_json(obj: lora.LoraOnDisk):
     }
 
 
-def api_loras(_: gr.Blocks, app: FastAPI):
+def api_networks(_: gr.Blocks, app: FastAPI):
     @app.get("/sdapi/v1/loras")
     async def get_loras():
-        return [create_lora_json(obj) for obj in lora.available_loras.values()]
+        return [create_lora_json(obj) for obj in networks.available_networks.values()]
 
     @app.post("/sdapi/v1/refresh-loras")
     async def refresh_loras():
-        return lora.list_available_loras()
+        return networks.list_available_networks()
 
 
-script_callbacks.on_app_started(api_loras)
+script_callbacks.on_app_started(api_networks)
 
 re_lora = re.compile("<lora:([^:]+):")
 
@@ -98,19 +105,19 @@ def infotext_pasted(infotext, d):
     hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
     hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
 
-    def lora_replacement(m):
+    def network_replacement(m):
         alias = m.group(1)
         shorthash = hashes.get(alias)
         if shorthash is None:
             return m.group(0)
 
-        lora_on_disk = lora.available_lora_hash_lookup.get(shorthash)
-        if lora_on_disk is None:
+        network_on_disk = networks.available_network_hash_lookup.get(shorthash)
+        if network_on_disk is None:
             return m.group(0)
 
-        return f'<lora:{lora_on_disk.get_alias()}:'
+        return f'<lora:{network_on_disk.get_alias()}:'
 
-    d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"])
+    d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
 
 
 script_callbacks.on_infotext_pasted(infotext_pasted)

extensions-builtin/Lora/ui_edit_user_metadata.py

@@ -1,3 +1,4 @@
+import datetime
 import html
 import random
 
@@ -46,14 +47,17 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
     def __init__(self, ui, tabname, page):
         super().__init__(ui, tabname, page)
 
+        self.select_sd_version = None
+
         self.taginfo = None
         self.edit_activation_text = None
         self.slider_preferred_weight = None
         self.edit_notes = None
 
-    def save_lora_user_metadata(self, name, desc, activation_text, preferred_weight, notes):
+    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
         user_metadata = self.get_user_metadata(name)
         user_metadata["description"] = desc
+        user_metadata["sd version"] = sd_version
         user_metadata["activation text"] = activation_text
         user_metadata["preferred weight"] = preferred_weight
         user_metadata["notes"] = notes
@@ -68,6 +72,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         keys = {
             'ss_sd_model_name': "Model:",
             'ss_clip_skip': "Clip skip:",
+            'ss_network_module': "Kohya module:",
         }
 
         for key, label in keys.items():
@@ -75,6 +80,10 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             if value is not None and str(value) != "None":
                 table.append((label, html.escape(value)))
 
+        ss_training_started_at = metadata.get('ss_training_started_at')
+        if ss_training_started_at:
+            table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
+
         ss_bucket_info = metadata.get("ss_bucket_info")
         if ss_bucket_info and "buckets" in ss_bucket_info:
             resolutions = {}
@@ -112,11 +121,11 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
 
         return [
-            *values[0:4],
+            *values[0:5],
+            item.get("sd_version", "Unknown"),
             gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
             user_metadata.get('activation text', ''),
             float(user_metadata.get('preferred weight', 0.0)),
-            user_metadata.get('notes', ''),
             gr.update(visible=True if tags else False),
             gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
         ]
@@ -141,10 +150,15 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
 
         return ", ".join(sorted(res))
 
+    def create_extra_default_items_in_left_column(self):
+
+        # this would be a lot better as gr.Radio but I can't make it work
+        self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
+
     def create_editor(self):
         self.create_default_editor_elems()
 
-        self.taginfo = gr.HighlightedText(label="Tags")
+        self.taginfo = gr.HighlightedText(label="Training dataset tags")
         self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
         self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
 
@@ -153,7 +167,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
                 random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
 
             with gr.Column(scale=1, min_width=120):
-                generate_random_prompt = gr.Button('Generate').style(full_width=True, size="lg")
+                generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
 
         self.edit_notes = gr.TextArea(label='Notes', lines=4)
 
@@ -178,10 +192,11 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             self.edit_description,
             self.html_filedata,
             self.html_preview,
+            self.edit_notes,
+            self.select_sd_version,
             self.taginfo,
             self.edit_activation_text,
             self.slider_preferred_weight,
-            self.edit_notes,
             row_random_prompt,
             random_prompt,
         ]
@@ -192,6 +207,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
 
         edited_components = [
             self.edit_description,
+            self.select_sd_version,
             self.edit_activation_text,
             self.slider_preferred_weight,
             self.edit_notes,

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -1,5 +1,7 @@
 import os
-import lora
+
+import network
+import networks
 
 from modules import shared, ui_extra_networks
 from modules.ui_extra_networks import quote_js
@@ -11,16 +13,15 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         super().__init__('Lora')
 
     def refresh(self):
-        lora.list_available_loras()
+        networks.list_available_networks()
 
-    def create_item(self, name, index=None):
-        lora_on_disk = lora.available_loras.get(name)
+    def create_item(self, name, index=None, enable_filter=True):
+        lora_on_disk = networks.available_networks.get(name)
 
         path, ext = os.path.splitext(lora_on_disk.filename)
 
         alias = lora_on_disk.get_alias()
 
-        # in 1.5 filename changes to be full filename instead of path without extension, and metadata is dict instead of json string
         item = {
             "name": name,
             "filename": lora_on_disk.filename,
@@ -30,6 +31,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
             "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)},
+            "sd_version": lora_on_disk.sd_version.name,
         }
 
         self.read_user_metadata(item)
@@ -40,15 +42,37 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         if activation_text:
             item["prompt"] += " + " + quote_js(" " + activation_text)
 
+        sd_version = item["user_metadata"].get("sd version")
+        if sd_version in network.SdVersion.__members__:
+            item["sd_version"] = sd_version
+            sd_version = network.SdVersion[sd_version]
+        else:
+            sd_version = lora_on_disk.sd_version
+
+        if shared.opts.lora_show_all or not enable_filter:
+            pass
+        elif sd_version == network.SdVersion.Unknown:
+            model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
+            if model_version.name in shared.opts.lora_hide_unknown_for_versions:
+                return None
+        elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
+            return None
+        elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
+            return None
+        elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
+            return None
+
         return item
 
     def list_items(self):
-        for index, name in enumerate(lora.available_loras):
+        for index, name in enumerate(networks.available_networks):
             item = self.create_item(name, index)
-            yield item
+
+            if item is not None:
+                yield item
 
     def allowed_directories_for_previews(self):
-        return [shared.cmd_opts.lora_dir]
+        return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
 
     def create_user_metadata_editor(self, ui, tabname):
         return LoraUserMetadataEditor(ui, tabname, self)

extensions-builtin/mobile/javascript/mobile.js

@@ -0,0 +1,26 @@
+var isSetupForMobile = false;
+
+function isMobile() {
+    for (var tab of ["txt2img", "img2img"]) {
+        var imageTab = gradioApp().getElementById(tab + '_results');
+        if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+function reportWindowSize() {
+    var currentlyMobile = isMobile();
+    if (currentlyMobile == isSetupForMobile) return;
+    isSetupForMobile = currentlyMobile;
+
+    for (var tab of ["txt2img", "img2img"]) {
+        var button = gradioApp().getElementById(tab + '_generate_box');
+        var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
+        target.insertBefore(button, target.firstElementChild);
+    }
+}
+
+window.addEventListener("resize", reportWindowSize);

html/extra-networks-card.html

@@ -1,8 +1,8 @@
 <div class='card' style={style} onclick={card_clicked} data-name="{name}" {sort_keys}>
 	{background_image}
 	<div class="button-row">
-		{edit_button}
 		{metadata_button}
+		{edit_button}
 	</div>
 	<div class='actions'>
 		<div class='additional'>

javascript/extraNetworks.js

@@ -211,7 +211,7 @@ function popup(contents) {
         globalPopupInner.classList.add('global-popup-inner');
         globalPopup.appendChild(globalPopupInner);
 
-        gradioApp().appendChild(globalPopup);
+        gradioApp().querySelector('.main').appendChild(globalPopup);
     }
 
     globalPopupInner.innerHTML = '';

javascript/hints.js

@@ -190,3 +190,14 @@ onUiUpdate(function(mutationRecords) {
         tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
     }
 });
+
+onUiLoaded(function() {
+    for (var comp of window.gradio_config.components) {
+        if (comp.props.webui_tooltip && comp.props.elem_id) {
+            var elem = gradioApp().getElementById(comp.props.elem_id);
+            if (elem) {
+                elem.title = comp.props.webui_tooltip;
+            }
+        }
+    }
+});

javascript/localization.js

@@ -11,11 +11,11 @@ var ignore_ids_for_localization = {
     train_hypernetwork: 'OPTION',
     txt2img_styles: 'OPTION',
     img2img_styles: 'OPTION',
-    setting_random_artist_categories: 'SPAN',
-    setting_face_restoration_model: 'SPAN',
-    setting_realesrgan_enabled_models: 'SPAN',
-    extras_upscaler_1: 'SPAN',
-    extras_upscaler_2: 'SPAN',
+    setting_random_artist_categories: 'OPTION',
+    setting_face_restoration_model: 'OPTION',
+    setting_realesrgan_enabled_models: 'OPTION',
+    extras_upscaler_1: 'OPTION',
+    extras_upscaler_2: 'OPTION',
 };
 
 var re_num = /^[.\d]+$/;

javascript/ui.js

@@ -152,7 +152,11 @@ function submit() {
     showSubmitButtons('txt2img', false);
 
     var id = randomId();
-    localStorage.setItem("txt2img_task_id", id);
+    try {
+        localStorage.setItem("txt2img_task_id", id);
+    } catch (e) {
+        console.warn(`Failed to save txt2img task id to localStorage: ${e}`);
+    }
 
     requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
         showSubmitButtons('txt2img', true);
@@ -171,7 +175,11 @@ function submit_img2img() {
     showSubmitButtons('img2img', false);
 
     var id = randomId();
-    localStorage.setItem("img2img_task_id", id);
+    try {
+        localStorage.setItem("img2img_task_id", id);
+    } catch (e) {
+        console.warn(`Failed to save img2img task id to localStorage: ${e}`);
+    }
 
     requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
         showSubmitButtons('img2img', true);
@@ -191,8 +199,6 @@ function restoreProgressTxt2img() {
     showRestoreProgressButton("txt2img", false);
     var id = localStorage.getItem("txt2img_task_id");
 
-    id = localStorage.getItem("txt2img_task_id");
-
     if (id) {
         requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
             showSubmitButtons('txt2img', true);

launch.py

@@ -1,6 +1,5 @@
 from modules import launch_utils
 
-
 args = launch_utils.args
 python = launch_utils.python
 git = launch_utils.git
@@ -18,6 +17,7 @@ run_pip = launch_utils.run_pip
 check_run_python = launch_utils.check_run_python
 git_clone = launch_utils.git_clone
 git_pull_recursive = launch_utils.git_pull_recursive
+list_extensions = launch_utils.list_extensions
 run_extension_installer = launch_utils.run_extension_installer
 prepare_environment = launch_utils.prepare_environment
 configure_for_tests = launch_utils.configure_for_tests
@@ -25,8 +25,11 @@ start = launch_utils.start
 
 
 def main():
-    if not args.skip_prepare_environment:
-        prepare_environment()
+    launch_utils.startup_timer.record("initial startup")
+
+    with launch_utils.startup_timer.subcategory("prepare environment"):
+        if not args.skip_prepare_environment:
+            prepare_environment()
 
     if args.test_server:
         configure_for_tests()

modules/api/api.py

@@ -15,7 +15,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -197,6 +197,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
         self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+        self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
         self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
         self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
@@ -333,14 +334,17 @@ class Api:
                 p.outpath_grids = opts.outdir_txt2img_grids
                 p.outpath_samples = opts.outdir_txt2img_samples
 
-                shared.state.begin(job="scripts_txt2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_txt2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
+                    shared.total_tqdm.clear()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -390,14 +394,17 @@ class Api:
                 p.outpath_grids = opts.outdir_img2img_grids
                 p.outpath_samples = opts.outdir_img2img_samples
 
-                shared.state.begin(job="scripts_img2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_img2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
+                    shared.total_tqdm.clear()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -604,6 +611,10 @@ class Api:
         with self.queue_lock:
             shared.refresh_checkpoints()
 
+    def refresh_vae(self):
+        with self.queue_lock:
+            shared_items.refresh_vae_list()
+
     def create_embedding(self, args: dict):
         try:
             shared.state.begin(job="create_embedding")
@@ -720,9 +731,9 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
-    def launch(self, server_name, port):
+    def launch(self, server_name, port, root_path):
         self.app.include_router(self.router)
-        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive)
+        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
 
     def kill_webui(self):
         restart.stop_program()

modules/api/models.py

@@ -1,4 +1,5 @@
 import inspect
+
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
@@ -207,11 +208,10 @@ class PreprocessResponse(BaseModel):
 fields = {}
 for key, metadata in opts.data_labels.items():
     value = opts.data.get(key)
-    optType = opts.typemap.get(type(metadata.default), type(value))
+    optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any
 
-    if (metadata is not None):
-        fields.update({key: (Optional[optType], Field(
-            default=metadata.default ,description=metadata.label))})
+    if metadata is not None:
+        fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
     else:
         fields.update({key: (Optional[optType], Field())})
 

modules/cache.py

@@ -1,6 +1,7 @@
 import json
 import os.path
 import threading
+import time
 
 from modules.paths import data_path, script_path
 
@@ -8,15 +9,37 @@ cache_filename = os.path.join(data_path, "cache.json")
 cache_data = None
 cache_lock = threading.Lock()
 
+dump_cache_after = None
+dump_cache_thread = None
+
 
 def dump_cache():
     """
-    Saves all cache data to a file.
+    Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written.
     """
 
+    global dump_cache_after
+    global dump_cache_thread
+
+    def thread_func():
+        global dump_cache_after
+        global dump_cache_thread
+
+        while dump_cache_after is not None and time.time() < dump_cache_after:
+            time.sleep(1)
+
+        with cache_lock:
+            with open(cache_filename, "w", encoding="utf8") as file:
+                json.dump(cache_data, file, indent=4)
+
+            dump_cache_after = None
+            dump_cache_thread = None
+
     with cache_lock:
-        with open(cache_filename, "w", encoding="utf8") as file:
-            json.dump(cache_data, file, indent=4)
+        dump_cache_after = time.time() + 5
+        if dump_cache_thread is None:
+            dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func)
+            dump_cache_thread.start()
 
 
 def cache(subsection):
@@ -84,7 +107,7 @@ def cached_data_for_file(subsection, title, filename, func):
         if ondisk_mtime > cached_mtime:
             entry = None
 
-    if not entry:
+    if not entry or 'value' not in entry:
         value = func()
         if value is None:
             return None

modules/call_queue.py

@@ -3,7 +3,7 @@ import html
 import threading
 import time
 
-from modules import shared, progress, errors
+from modules import shared, progress, errors, devices
 
 queue_lock = threading.Lock()
 
@@ -75,6 +75,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
             error_message = f'{type(e).__name__}: {e}'
             res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
 
+        devices.torch_gc()
+
         shared.state.skipped = False
         shared.state.interrupted = False
         shared.state.job_count = 0

modules/cmd_args.py

@@ -13,8 +13,10 @@ parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
 parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
 parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
 parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
@@ -65,6 +67,7 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -109,3 +112,5 @@ parser.add_argument('--subpath', type=str, help='customize the subpath for gradi
 parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
 parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
 parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
+parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
+parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)

modules/devices.py

@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors
+from modules import errors, rng_philox
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -71,14 +71,17 @@ def enable_tf32():
         torch.backends.cudnn.allow_tf32 = True
 
 
-
 errors.run(enable_tf32, "Enabling TF32")
 
-cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
-dtype = torch.float16
-dtype_vae = torch.float16
-dtype_unet = torch.float16
+cpu: torch.device = torch.device("cpu")
+device: torch.device = None
+device_interrogate: torch.device = None
+device_gfpgan: torch.device = None
+device_esrgan: torch.device = None
+device_codeformer: torch.device = None
+dtype: torch.dtype = torch.float16
+dtype_vae: torch.dtype = torch.float16
+dtype_unet: torch.dtype = torch.float16
 unet_needs_upcast = False
 
 
@@ -90,23 +93,87 @@ def cond_cast_float(input):
     return input.float() if unet_needs_upcast else input
 
 
+nv_rng = None
+
+
 def randn(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
+
     from modules.shared import opts
 
-    torch.manual_seed(seed)
+    manual_seed(seed)
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
     if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
+
     return torch.randn(shape, device=device)
 
 
+def randn_local(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        rng = rng_philox.Generator(seed)
+        return torch.asarray(rng.randn(shape), device=device)
+
+    local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device
+    local_generator = torch.Generator(local_device).manual_seed(int(seed))
+    return torch.randn(shape, device=local_device, generator=local_generator).to(device)
+
+
+def randn_like(x):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
+
+    if opts.randn_source == "CPU" or x.device.type == 'mps':
+        return torch.randn_like(x, device=cpu).to(x.device)
+
+    return torch.randn_like(x)
+
+
 def randn_without_seed(shape):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
     from modules.shared import opts
 
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
     if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
+
     return torch.randn(shape, device=device)
 
 
+def manual_seed(seed):
+    """Set up a global random number generator using the specified seed."""
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        global nv_rng
+        nv_rng = rng_philox.Generator(seed)
+        return
+
+    torch.manual_seed(seed)
+
+
 def autocast(disable=False):
     from modules import shared
 

modules/errors.py

@@ -14,7 +14,8 @@ def record_exception():
     if exception_records and exception_records[-1] == e:
         return
 
-    exception_records.append((e, tb))
+    from modules import sysinfo
+    exception_records.append(sysinfo.format_exception(e, tb))
 
     if len(exception_records) > 5:
         exception_records.pop(0)
@@ -83,3 +84,53 @@ def run(code, task):
         code()
     except Exception as e:
         display(task, e)
+
+
+def check_versions():
+    from packaging import version
+    from modules import shared
+
+    import torch
+    import gradio
+
+    expected_torch_version = "2.0.0"
+    expected_xformers_version = "0.0.20"
+    expected_gradio_version = "3.39.0"
+
+    if version.parse(torch.__version__) < version.parse(expected_torch_version):
+        print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+
+    if shared.xformers_available:
+        import xformers
+
+        if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+            print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
+            """.strip())
+
+    if gradio.__version__ != expected_gradio_version:
+        print_error_explanation(f"""
+You are running gradio {gradio.__version__}.
+The program is designed to work with gradio {expected_gradio_version}.
+Using a different version of gradio is extremely likely to break the program.
+
+Reasons why you have the mismatched gradio version can be:
+  - you use --skip-install flag.
+  - you use webui.py to start the program instead of launch.py.
+  - an extension installs the incompatible gradio version.
+
+Use --skip-version-check commandline argument to disable this check.
+        """.strip())
+

modules/extensions.py

@@ -11,9 +11,9 @@ os.makedirs(extensions_dir, exist_ok=True)
 
 
 def active():
-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
         return []
-    elif shared.opts.disable_all_extensions == "extra":
+    elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra":
         return [x for x in extensions if x.enabled and x.is_builtin]
     else:
         return [x for x in extensions if x.enabled]
@@ -56,10 +56,12 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
-
-        d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
-        self.from_dict(d)
-        self.status = 'unknown'
+        try:
+            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+            self.from_dict(d)
+        except FileNotFoundError:
+            pass
+        self.status = 'unknown' if self.status == '' else self.status
 
     def do_read_info_from_repo(self):
         repo = None
@@ -139,8 +141,12 @@ def list_extensions():
     if not os.path.isdir(extensions_dir):
         return
 
-    if shared.opts.disable_all_extensions == "all":
+    if shared.cmd_opts.disable_all_extensions:
+        print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
+    elif shared.opts.disable_all_extensions == "all":
         print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+    elif shared.cmd_opts.disable_extra_extensions:
+        print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***")
     elif shared.opts.disable_all_extensions == "extra":
         print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
 

modules/extra_networks.py

@@ -1,19 +1,27 @@
+import json
+import os
 import re
 from collections import defaultdict
 
 from modules import errors
 
 extra_network_registry = {}
+extra_network_aliases = {}
 
 
 def initialize():
     extra_network_registry.clear()
+    extra_network_aliases.clear()
 
 
 def register_extra_network(extra_network):
     extra_network_registry[extra_network.name] = extra_network
 
 
+def register_extra_network_alias(extra_network, alias):
+    extra_network_aliases[alias] = extra_network
+
+
 def register_default_extra_networks():
     from modules.extra_networks_hypernet import ExtraNetworkHypernet
     register_extra_network(ExtraNetworkHypernet())
@@ -82,20 +90,26 @@ def activate(p, extra_network_data):
     """call activate for extra networks in extra_network_data in specified order, then call
     activate for all remaining registered networks with an empty argument list"""
 
+    activated = []
+
     for extra_network_name, extra_network_args in extra_network_data.items():
         extra_network = extra_network_registry.get(extra_network_name, None)
+
+        if extra_network is None:
+            extra_network = extra_network_aliases.get(extra_network_name, None)
+
         if extra_network is None:
             print(f"Skipping unknown extra network: {extra_network_name}")
             continue
 
         try:
             extra_network.activate(p, extra_network_args)
+            activated.append(extra_network)
         except Exception as e:
             errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
-        args = extra_network_data.get(extra_network_name, None)
-        if args is not None:
+        if extra_network in activated:
             continue
 
         try:
@@ -165,3 +179,20 @@ def parse_prompts(prompts):
 
     return res, extra_data
 
+
+def get_user_metadata(filename):
+    if filename is None:
+        return {}
+
+    basename, ext = os.path.splitext(filename)
+    metadata_filename = basename + '.json'
+
+    metadata = {}
+    try:
+        if os.path.isfile(metadata_filename):
+            with open(metadata_filename, "r", encoding="utf8") as file:
+                metadata = json.load(file)
+    except Exception as e:
+        errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+
+    return metadata

modules/extras.py

@@ -7,7 +7,7 @@ import json
 import torch
 import tqdm
 
-from modules import shared, images, sd_models, sd_vae, sd_models_config
+from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
 from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
@@ -72,7 +72,20 @@ def to_half(tensor, enable):
     return tensor
 
 
-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
+def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name):
+    metadata = {}
+
+    for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]:
+        checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None)
+        if checkpoint_info is None:
+            continue
+
+        metadata.update(checkpoint_info.metadata)
+
+    return json.dumps(metadata, indent=4, ensure_ascii=False)
+
+
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json):
     shared.state.begin(job="model-merge")
 
     def fail(message):
@@ -241,11 +254,25 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     shared.state.textinfo = "Saving"
     print(f"Saving to {output_modelname}...")
 
-    metadata = None
+    metadata = {}
+
+    if save_metadata and copy_metadata_fields:
+        if primary_model_info:
+            metadata.update(primary_model_info.metadata)
+        if secondary_model_info:
+            metadata.update(secondary_model_info.metadata)
+        if tertiary_model_info:
+            metadata.update(tertiary_model_info.metadata)
 
     if save_metadata:
-        metadata = {"format": "pt"}
+        try:
+            metadata.update(json.loads(metadata_json))
+        except Exception as e:
+            errors.display(e, "readin metadata from json")
 
+        metadata["format"] = "pt"
+
+    if save_metadata and add_merge_recipe:
         merge_recipe = {
             "type": "webui", # indicate this model was merged with webui's built-in merger
             "primary_model_hash": primary_model_info.sha256,
@@ -261,7 +288,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
             "is_inpainting": result_is_inpainting_model,
             "is_instruct_pix2pix": result_is_instruct_pix2pix_model
         }
-        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
 
         sd_merge_models = {}
 
@@ -281,11 +307,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         if tertiary_model_info:
             add_model_metadata(tertiary_model_info)
 
+        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
         metadata["sd_merge_models"] = json.dumps(sd_merge_models)
 
     _, extension = os.path.splitext(output_modelname)
     if extension.lower() == ".safetensors":
-        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
+        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None)
     else:
         torch.save(theta_0, output_modelname)
 

modules/generation_parameters_copypaste.py

@@ -280,6 +280,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Hires sampler" not in res:
         res["Hires sampler"] = "Use same sampler"
 
+    if "Hires checkpoint" not in res:
+        res["Hires checkpoint"] = "Use same checkpoint"
+
     if "Hires prompt" not in res:
         res["Hires prompt"] = ""
 

modules/gradio_extensons.py

@@ -0,0 +1,60 @@
+import gradio as gr
+
+from modules import scripts
+
+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+    self.webui_tooltip = kwargs.pop('tooltip', None)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.before_component(self, **kwargs)
+
+    scripts.script_callbacks.before_component_callback(self, **kwargs)
+
+    res = original_IOComponent_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+    if scripts.scripts_current is not None:
+        scripts.scripts_current.after_component(self, **kwargs)
+
+    return res
+
+
+def Block_get_config(self):
+    config = original_Block_get_config(self)
+
+    webui_tooltip = getattr(self, 'webui_tooltip', None)
+    if webui_tooltip:
+        config["webui_tooltip"] = webui_tooltip
+
+    return config
+
+
+def BlockContext_init(self, *args, **kwargs):
+    res = original_BlockContext_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    return res
+
+
+original_IOComponent_init = gr.components.IOComponent.__init__
+original_Block_get_config = gr.blocks.Block.get_config
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+
+gr.components.IOComponent.__init__ = IOComponent_init
+gr.blocks.Block.get_config = Block_get_config
+gr.blocks.BlockContext.__init__ = BlockContext_init

modules/hypernetworks/hypernetwork.py

@@ -10,7 +10,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
+from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
 from modules.textual_inversion import textual_inversion, logging
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -378,7 +378,7 @@ def apply_hypernetworks(hypernetworks, context, layer=None):
     return context_k, context_v
 
 
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
+def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs):
     h = self.heads
 
     q = self.to_q(x)
@@ -469,8 +469,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
 
 
 def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
-    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
-    from modules import images
+    from modules import images, processing
 
     save_hypernetwork_every = save_hypernetwork_every or 0
     create_image_every = create_image_every or 0

modules/images.py

@@ -318,7 +318,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
     return res
 
 
-invalid_filename_chars = '<>:"/\\|?*\n'
+invalid_filename_chars = '<>:"/\\|?*\n\r\t'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@@ -363,7 +363,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
-        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
+        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
         'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
         'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
         'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),

modules/img2img.py

@@ -3,14 +3,13 @@ from contextlib import closing
 from pathlib import Path
 
 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
 import gradio as gr
 
 from modules import sd_samplers, images as imgutil
 from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
-from modules.images import save_image
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
@@ -18,9 +17,10 @@ import modules.scripts
 
 
 def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+    output_dir = output_dir.strip()
     processing.fix_seed(p)
 
-    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp")))
+    images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
 
     is_inpaint_batch = False
     if inpaint_mask_dir:
@@ -32,11 +32,6 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 
-    save_normally = output_dir == ''
-
-    p.do_not_save_grid = True
-    p.do_not_save_samples = not save_normally
-
     state.job_count = len(images) * p.n_iter
 
     # extract "default" params to use in case getting png info fails
@@ -111,21 +106,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
 
         proc = modules.scripts.scripts_img2img.run(p, *args)
         if proc is None:
-            proc = process_images(p)
-
-        for n, processed_image in enumerate(proc.images):
-            filename = image_path.stem
-            infotext = proc.infotext(p, n)
-            relpath = os.path.dirname(os.path.relpath(image, input_dir))
-
-            if n > 0:
-                filename += f"-{n}"
-
-            if not save_normally:
-                os.makedirs(os.path.join(output_dir, relpath), exist_ok=True)
-                if processed_image.mode == 'RGBA':
-                    processed_image = processed_image.convert("RGB")
-                save_image(processed_image, os.path.join(output_dir, relpath), None, extension=opts.samples_format, info=infotext, forced_filename=filename, save_to_dirs=False)
+            if output_dir:
+                p.outpath_samples = output_dir
+                p.override_settings['save_to_dirs'] = False
+                if p.n_iter > 1 or p.batch_size > 1:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+                else:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+            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_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, 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):
@@ -141,9 +129,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         mask = None
     elif mode == 2:  # inpaint
         image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
-        alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-        mask = mask.convert('L').point(lambda x: 255 if x > 128 else 0, mode='1')
-        mask = ImageChops.lighter(alpha_mask, mask).convert('L')
+        mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
         image = image.convert("RGB")
     elif mode == 3:  # inpaint sketch
         image = inpaint_color_sketch

modules/launch_utils.py

@@ -1,4 +1,5 @@
 # this scripts installs necessary requirements and launches main program in webui.py
+import re
 import subprocess
 import os
 import sys
@@ -9,6 +10,7 @@ from functools import lru_cache
 
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
+from modules.timer import startup_timer
 
 args, _ = cmd_args.parser.parse_known_args()
 
@@ -192,7 +194,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = os.path.abspath(".")
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
@@ -222,8 +224,51 @@ def run_extensions_installers(settings_file):
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+    with startup_timer.subcategory("run extensions installers"):
+        for dirname_extension in list_extensions(settings_file):
+            path = os.path.join(extensions_dir, dirname_extension)
+
+            if os.path.isdir(path):
+                run_extension_installer(path)
+                startup_timer.record(dirname_extension)
+
+
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
+
+
+def requirements_met(requirements_file):
+    """
+    Does a simple parse of a requirements.txt file to determine if all rerqirements in it
+    are already installed. Returns True if so, False if not installed or parsing fails.
+    """
+
+    import importlib.metadata
+    import packaging.version
+
+    with open(requirements_file, "r", encoding="utf8") as file:
+        for line in file:
+            if line.strip() == "":
+                continue
+
+            m = re.match(re_requirement, line)
+            if m is None:
+                return False
+
+            package = m.group(1).strip()
+            version_required = (m.group(2) or "").strip()
+
+            if version_required == "":
+                continue
+
+            try:
+                version_installed = importlib.metadata.version(package)
+            except Exception:
+                return False
+
+            if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+                return False
+
+    return True
 
 
 def prepare_environment():
@@ -237,11 +282,13 @@ def prepare_environment():
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+    stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
     codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     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")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
@@ -249,15 +296,18 @@ def prepare_environment():
     try:
         # the existance 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')
+        os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:
         pass
 
     if not args.skip_python_version_check:
         check_python_version()
 
+    startup_timer.record("checks")
+
     commit = commit_hash()
     tag = git_tag()
+    startup_timer.record("git version info")
 
     print(f"Python {sys.version}")
     print(f"Version: {tag}")
@@ -265,21 +315,27 @@ def prepare_environment():
 
     if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
         run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+        startup_timer.record("install torch")
 
     if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
         raise RuntimeError(
             'Torch is not able to use GPU; '
             'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
         )
+    startup_timer.record("torch GPU test")
+
 
     if not is_installed("gfpgan"):
         run_pip(f"install {gfpgan_package}", "gfpgan")
+        startup_timer.record("install gfpgan")
 
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
+        startup_timer.record("install clip")
 
     if not is_installed("open_clip"):
         run_pip(f"install {openclip_package}", "open_clip")
+        startup_timer.record("install open_clip")
 
     if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
         if platform.system() == "Windows":
@@ -293,36 +349,49 @@ def prepare_environment():
         elif platform.system() == "Linux":
             run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
 
+        startup_timer.record("install xformers")
+
     if not is_installed("ngrok") and args.ngrok:
         run_pip("install ngrok", "ngrok")
+        startup_timer.record("install ngrok")
 
     os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
 
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
     git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
     git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
     git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
+    startup_timer.record("clone repositores")
+
     if not is_installed("lpips"):
         run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+        startup_timer.record("install CodeFormer requirements")
 
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+    if not requirements_met(requirements_file):
+        run_pip(f"install -r \"{requirements_file}\"", "requirements")
+        startup_timer.record("install requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)
 
     if args.update_check:
         version_check(commit)
+        startup_timer.record("check version")
 
     if args.update_all_extensions:
         git_pull_recursive(extensions_dir)
+        startup_timer.record("update extensions")
 
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
 
 
+
 def configure_for_tests():
     if "--api" not in sys.argv:
         sys.argv.append("--api")

modules/lowvram.py

@@ -15,6 +15,9 @@ def send_everything_to_cpu():
 
 
 def setup_for_low_vram(sd_model, use_medvram):
+    if getattr(sd_model, 'lowvram', False):
+        return
+
     sd_model.lowvram = True
 
     parents = {}
@@ -53,19 +56,50 @@ def setup_for_low_vram(sd_model, use_medvram):
         send_me_to_gpu(first_stage_model, None)
         return first_stage_model_decode(z)
 
-    # for SD1, cond_stage_model is CLIP and its NN is in the tranformer frield, but for SD2, it's open clip, and it's in model field
-    if hasattr(sd_model.cond_stage_model, 'model'):
-        sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
+    to_remain_in_cpu = [
+        (sd_model, 'first_stage_model'),
+        (sd_model, 'depth_model'),
+        (sd_model, 'embedder'),
+        (sd_model, 'model'),
+        (sd_model, 'embedder'),
+    ]
 
-    # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
-    # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
+    is_sdxl = hasattr(sd_model, 'conditioner')
+    is_sd2 = not is_sdxl and hasattr(sd_model.cond_stage_model, 'model')
+
+    if is_sdxl:
+        to_remain_in_cpu.append((sd_model, 'conditioner'))
+    elif is_sd2:
+        to_remain_in_cpu.append((sd_model.cond_stage_model, 'model'))
+    else:
+        to_remain_in_cpu.append((sd_model.cond_stage_model, 'transformer'))
+
+    # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model
+    stored = []
+    for obj, field in to_remain_in_cpu:
+        module = getattr(obj, field, None)
+        stored.append(module)
+        setattr(obj, field, None)
+
+    # send the model to GPU.
     sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
+
+    # put modules back. the modules will be in CPU.
+    for (obj, field), module in zip(to_remain_in_cpu, stored):
+        setattr(obj, field, module)
 
     # register hooks for those the first three models
-    sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+    if is_sdxl:
+        sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
+    elif is_sd2:
+        sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+        sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+        parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
+    else:
+        sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
+
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
     sd_model.first_stage_model.decode = first_stage_model_decode_wrap
@@ -73,11 +107,6 @@ def setup_for_low_vram(sd_model, use_medvram):
         sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
     if sd_model.embedder:
         sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
-    parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
-    if hasattr(sd_model.cond_stage_model, 'model'):
-        sd_model.cond_stage_model.model = sd_model.cond_stage_model.transformer
-        del sd_model.cond_stage_model.transformer
 
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)

modules/paths.py

@@ -5,6 +5,21 @@ from modules.paths_internal import models_path, script_path, data_path, extensio
 import modules.safe  # noqa: F401
 
 
+def mute_sdxl_imports():
+    """create fake modules that SDXL wants to import but doesn't actually use for our purposes"""
+
+    class Dummy:
+        pass
+
+    module = Dummy()
+    module.LPIPS = None
+    sys.modules['taming.modules.losses.lpips'] = module
+
+    module = Dummy()
+    module.StableDataModuleFromConfig = None
+    sys.modules['sgm.data'] = module
+
+
 # data_path = cmd_opts_pre.data
 sys.path.insert(0, script_path)
 
@@ -18,8 +33,11 @@ for possible_sd_path in possible_sd_paths:
 
 assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
 
+mute_sdxl_imports()
+
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),
+    (os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
     (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
@@ -35,6 +53,13 @@ for d, must_exist, what, options in path_dirs:
         d = os.path.abspath(d)
         if "atstart" in options:
             sys.path.insert(0, d)
+        elif "sgm" in options:
+            # Stable Diffusion XL repo has scripts dir with __init__.py in it which ruins every extension's scripts dir, so we
+            # import sgm and remove it from sys.path so that when a script imports scripts.something, it doesbn't use sgm's scripts dir.
+
+            sys.path.insert(0, d)
+            import sgm  # noqa: F401
+            sys.path.pop(0)
         else:
             sys.path.append(d)
         paths[what] = d

modules/processing.py

@@ -14,7 +14,7 @@ from skimage import exposure
 from typing import Any, Dict, List
 
 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
+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
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -30,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
+decode_first_stage = sd_samplers_common.decode_first_stage
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
@@ -330,8 +331,21 @@ class StableDiffusionProcessing:
 
         caches is a list with items described above.
         """
+
+        cached_params = (
+            required_prompts,
+            steps,
+            opts.CLIP_stop_at_last_layers,
+            shared.sd_model.sd_checkpoint_info,
+            extra_network_data,
+            opts.sdxl_crop_left,
+            opts.sdxl_crop_top,
+            self.width,
+            self.height,
+        )
+
         for cache in caches:
-            if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
+            if cache[0] is not None and cached_params == cache[0]:
                 return cache[1]
 
         cache = caches[0]
@@ -339,14 +353,17 @@ class StableDiffusionProcessing:
         with devices.autocast():
             cache[1] = function(shared.sd_model, required_prompts, steps)
 
-        cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data)
+        cache[0] = cached_params
         return cache[1]
 
     def setup_conds(self):
+        prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
+        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.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)
 
     def parse_extra_network_prompts(self):
         self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
@@ -476,7 +493,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
 
         subnoise = None
-        if subseeds is not None:
+        if subseeds is not None and subseed_strength != 0:
             subseed = 0 if i >= len(subseeds) else subseeds[i]
 
             subnoise = devices.randn(subseed, noise_shape)
@@ -508,7 +525,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
             cnt = p.sampler.number_of_needed_noises(p)
 
             if eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + eta_noise_seed_delta)
+                devices.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@@ -522,11 +539,42 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
-def decode_first_stage(model, x):
-    with devices.autocast(disable=x.dtype == devices.dtype_vae):
-        x = model.decode_first_stage(x)
+class DecodedSamples(list):
+    already_decoded = True
 
-    return x
+
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+    samples = DecodedSamples()
+
+    for i in range(batch.shape[0]):
+        sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if check_for_nans:
+            try:
+                devices.test_for_nans(sample, "vae")
+            except devices.NansException as e:
+                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+                    raise e
+
+                errors.print_error_explanation(
+                    "A tensor with all NaNs was produced in VAE.\n"
+                    "Web UI will now convert VAE into 32-bit float and retry.\n"
+                    "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+                )
+
+                devices.dtype_vae = torch.float32
+                model.first_stage_model.to(devices.dtype_vae)
+                batch = batch.to(devices.dtype_vae)
+
+                sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if target_device is not None:
+            sample = sample.to(target_device)
+
+        samples.append(sample)
+
+    return samples
 
 
 def get_fixed_seed(seed):
@@ -551,8 +599,12 @@ def program_version():
     return res
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):
-    index = position_in_batch + iteration * p.batch_size
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+    if index is None:
+        index = position_in_batch + iteration * p.batch_size
+
+    if all_negative_prompts is None:
+        all_negative_prompts = p.all_negative_prompts
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
     enable_hr = getattr(p, 'enable_hr', False)
@@ -568,12 +620,12 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Sampler": p.sampler_name,
         "CFG scale": p.cfg_scale,
         "Image CFG scale": getattr(p, 'image_cfg_scale', None),
-        "Seed": all_seeds[index],
+        "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 or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
+        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
+        "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}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
@@ -583,7 +635,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
         "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
-        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "RNG": opts.randn_source if opts.randn_source != "GPU" and opts.randn_source != "NV" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
         **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
@@ -593,7 +645,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.prompt if use_main_prompt else all_prompts[index]
-    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
+    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""
 
     return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
 
@@ -667,9 +719,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
-    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
-
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
@@ -743,9 +792,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
-            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
-            for x in x_samples_ddim:
-                devices.test_for_nans(x, "vae")
+            if getattr(samples_ddim, 'already_decoded', False):
+                x_samples_ddim = samples_ddim
+            else:
+                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
 
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@@ -760,6 +810,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
+                p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+                p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+                x_samples_ddim = batch_params.images
+
+            def infotext(index=0, use_main_prompt=False):
+                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
@@ -768,7 +828,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -785,15 +845,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
 
-                text = infotext(n, i)
+                text = infotext(i)
                 infotexts.append(text)
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
@@ -804,10 +864,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
 
                     if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
 
                     if opts.return_mask:
                         output_images.append(image_mask)
@@ -848,7 +908,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p,
         images_list=output_images,
         seed=p.all_seeds[0],
-        info=infotext(),
+        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,
@@ -878,7 +938,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     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_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
+    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
@@ -889,11 +949,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         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
 
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -916,6 +979,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
+            if self.hr_checkpoint_name:
+                self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+                if self.hr_checkpoint_info is None:
+                    raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+                self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
             if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
                 self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
 
@@ -925,6 +996,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
                 self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
 
+            self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+            if self.enable_hr and self.latent_scale_mode is None:
+                if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+                    raise Exception(f"could not find upscaler named {self.hr_upscaler}")
+
             if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
                 self.hr_resize_x = self.width
                 self.hr_resize_y = self.height
@@ -963,14 +1039,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
                     self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
 
-            # special case: the user has chosen to do nothing
-            if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
-                self.enable_hr = False
-                self.denoising_strength = None
-                self.extra_generation_params.pop("Hires upscale", None)
-                self.extra_generation_params.pop("Hires resize", None)
-                return
-
             if not state.processing_has_refined_job_count:
                 if state.job_count == -1:
                     state.job_count = self.n_iter
@@ -988,17 +1056,32 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
-        if self.enable_hr and latent_scale_mode is None:
-            if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
-                raise Exception(f"could not find upscaler named {self.hr_upscaler}")
-
         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+        del x
 
         if not self.enable_hr:
             return samples
 
+        if self.latent_scale_mode is None:
+            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+        else:
+            decoded_samples = None
+
+        current = shared.sd_model.sd_checkpoint_info
+        try:
+            if self.hr_checkpoint_info is not None:
+                self.sampler = None
+                sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+                devices.torch_gc()
+
+            return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
+        finally:
+            self.sampler = None
+            sd_models.reload_model_weights(info=current)
+            devices.torch_gc()
+
+    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
         self.is_hr_pass = True
 
         target_width = self.hr_upscale_to_x
@@ -1014,13 +1097,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
 
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
 
-        if latent_scale_mode is not None:
+        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+            img2img_sampler_name = 'DDIM'
+
+        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
+
+        if self.latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
@@ -1029,7 +1119,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 image_conditioning = self.txt2img_image_conditioning(samples)
         else:
-            decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
             batch_images = []
@@ -1048,6 +1137,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = torch.from_numpy(np.array(batch_images))
             decoded_samples = decoded_samples.to(shared.device)
             decoded_samples = 2. * decoded_samples - 1.
+            decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
@@ -1055,19 +1145,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
-
-        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
-            img2img_sampler_name = 'DDIM'
-
-        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
-
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
-        x = None
         devices.torch_gc()
 
         if not self.disable_extra_networks:
@@ -1086,9 +1168,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
+        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
         self.is_hr_pass = False
 
-        return samples
+        return decoded_samples
 
     def close(self):
         super().close()
@@ -1127,8 +1211,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         if self.hr_c is not None:
             return
 
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [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, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        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)
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [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.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
 
     def setup_conds(self):
         super().setup_conds()
@@ -1136,7 +1223,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_uc = None
         self.hr_c = None
 
-        if self.enable_hr:
+        if self.enable_hr and self.hr_checkpoint_info is None:
             if shared.opts.hires_fix_use_firstpass_conds:
                 self.calculate_hr_conds()
 
@@ -1288,9 +1375,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
         image = torch.from_numpy(batch_images)
         image = 2. * image - 1.
-        image = image.to(shared.device)
+        image = image.to(shared.device, dtype=devices.dtype_vae)
 
         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
+        devices.torch_gc()
 
         if self.resize_mode == 3:
             self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

modules/prompt_parser.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import re
 from collections import namedtuple
 from typing import List
@@ -17,8 +19,8 @@ prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
 !emphasized: "(" prompt ")"
         | "(" prompt ":" prompt ")"
         | "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
-alternate: "[" prompt ("|" prompt)+ "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
+alternate: "[" prompt ("|" [prompt])+ "]"
 WHITESPACE: /\s+/
 plain: /([^\\\[\]():|]|\\.)+/
 %import common.SIGNED_NUMBER -> NUMBER
@@ -51,6 +53,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     [[3, '((a][:b:c '], [10, '((a][:b:c d']]
     >>> g("[a|(b:1.1)]")
     [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+    >>> g("[fe|]male")
+    [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
+    >>> g("[fe|||]male")
+    [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
     """
 
     def collect_steps(steps, tree):
@@ -58,11 +64,11 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
 
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
-                tree.children[-1] = float(tree.children[-1])
-                if tree.children[-1] < 1:
-                    tree.children[-1] *= steps
-                tree.children[-1] = min(steps, int(tree.children[-1]))
-                res.append(tree.children[-1])
+                tree.children[-2] = float(tree.children[-2])
+                if tree.children[-2] < 1:
+                    tree.children[-2] *= steps
+                tree.children[-2] = min(steps, int(tree.children[-2]))
+                res.append(tree.children[-2])
 
             def alternate(self, tree):
                 res.extend(range(1, steps+1))
@@ -73,10 +79,11 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     def at_step(step, tree):
         class AtStep(lark.Transformer):
             def scheduled(self, args):
-                before, after, _, when = args
+                before, after, _, when, _ = args
                 yield before or () if step <= when else after
             def alternate(self, args):
-                yield next(args[(step - 1)%len(args)])
+                args = ["" if not arg else arg for arg in args]
+                yield args[(step - 1) % len(args)]
             def start(self, args):
                 def flatten(x):
                     if type(x) == str:
@@ -109,7 +116,25 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
 ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
 
 
-def get_learned_conditioning(model, prompts, steps):
+class SdConditioning(list):
+    """
+    A list with prompts for stable diffusion's conditioner model.
+    Can also specify width and height of created image - SDXL needs it.
+    """
+    def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None):
+        super().__init__()
+        self.extend(prompts)
+
+        if copy_from is None:
+            copy_from = prompts
+
+        self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False)
+        self.width = width or getattr(copy_from, 'width', None)
+        self.height = height or getattr(copy_from, 'height', None)
+
+
+
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
     """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
     and the sampling step at which this condition is to be replaced by the next one.
 
@@ -139,12 +164,17 @@ def get_learned_conditioning(model, prompts, steps):
             res.append(cached)
             continue
 
-        texts = [x[1] for x in prompt_schedule]
+        texts = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
         conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
         for i, (end_at_step, _) in enumerate(prompt_schedule):
-            cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
+            if isinstance(conds, dict):
+                cond = {k: v[i] for k, v in conds.items()}
+            else:
+                cond = conds[i]
+
+            cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
 
         cache[prompt] = cond_schedule
         res.append(cond_schedule)
@@ -153,13 +183,15 @@ def get_learned_conditioning(model, prompts, steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
-def get_multicond_prompt_list(prompts):
+
+def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
     res_indexes = []
 
-    prompt_flat_list = []
     prompt_indexes = {}
+    prompt_flat_list = SdConditioning(prompts)
+    prompt_flat_list.clear()
 
     for prompt in prompts:
         subprompts = re_AND.split(prompt)
@@ -196,6 +228,7 @@ class MulticondLearnedConditioning:
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
         self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
 
+
 def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
     """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
     For each prompt, the list is obtained by splitting the prompt using the AND separator.
@@ -214,20 +247,57 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
+class DictWithShape(dict):
+    def __init__(self, x, shape):
+        super().__init__()
+        self.update(x)
+
+    @property
+    def shape(self):
+        return self["crossattn"].shape
+
+
 def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
-    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+    is_dict = isinstance(param, dict)
+
+    if is_dict:
+        dict_cond = param
+        res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
+        res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
+    else:
+        res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+
     for i, cond_schedule in enumerate(c):
         target_index = 0
         for current, entry in enumerate(cond_schedule):
             if current_step <= entry.end_at_step:
                 target_index = current
                 break
-        res[i] = cond_schedule[target_index].cond
+
+        if is_dict:
+            for k, param in cond_schedule[target_index].cond.items():
+                res[k][i] = param
+        else:
+            res[i] = cond_schedule[target_index].cond
 
     return res
 
 
+def stack_conds(tensors):
+    # if prompts have wildly different lengths above the limit we'll get tensors of different shapes
+    # and won't be able to torch.stack them. So this fixes that.
+    token_count = max([x.shape[0] for x in tensors])
+    for i in range(len(tensors)):
+        if tensors[i].shape[0] != token_count:
+            last_vector = tensors[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
+    return torch.stack(tensors)
+
+
+
 def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     param = c.batch[0][0].schedules[0].cond
 
@@ -249,16 +319,14 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
 
         conds_list.append(conds_for_batch)
 
-    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
-    # and won't be able to torch.stack them. So this fixes that.
-    token_count = max([x.shape[0] for x in tensors])
-    for i in range(len(tensors)):
-        if tensors[i].shape[0] != token_count:
-            last_vector = tensors[i][-1:]
-            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
-            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+    if isinstance(tensors[0], dict):
+        keys = list(tensors[0].keys())
+        stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
+        stacked = DictWithShape(stacked, stacked['crossattn'].shape)
+    else:
+        stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
 
-    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+    return conds_list, stacked
 
 
 re_attention = re.compile(r"""
@@ -270,7 +338,7 @@ re_attention = re.compile(r"""
 \\|
 \(|
 \[|
-:([+-]?[.\d]+)\)|
+:\s*([+-]?[.\d]+)\s*\)|
 \)|
 ]|
 [^\\()\[\]:]+|

modules/rng_philox.py

@@ -0,0 +1,102 @@
+"""RNG imitiating torch cuda randn on CPU. You are welcome.
+
+Usage:
+
+```
+g = Generator(seed=0)
+print(g.randn(shape=(3, 4)))
+```
+
+Expected output:
+```
+[[-0.92466259 -0.42534415 -2.6438457   0.14518388]
+ [-0.12086647 -0.57972564 -0.62285122 -0.32838709]
+ [-1.07454231 -0.36314407 -1.67105067  2.26550497]]
+```
+"""
+
+import numpy as np
+
+philox_m = [0xD2511F53, 0xCD9E8D57]
+philox_w = [0x9E3779B9, 0xBB67AE85]
+
+two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
+two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
+
+
+def uint32(x):
+    """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
+    return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
+
+
+def philox4_round(counter, key):
+    """A single round of the Philox 4x32 random number generator."""
+
+    v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
+    v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
+
+    counter[0] = v2[1] ^ counter[1] ^ key[0]
+    counter[1] = v2[0]
+    counter[2] = v1[1] ^ counter[3] ^ key[1]
+    counter[3] = v1[0]
+
+
+def philox4_32(counter, key, rounds=10):
+    """Generates 32-bit random numbers using the Philox 4x32 random number generator.
+
+    Parameters:
+        counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
+        key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
+        rounds (int): The number of rounds to perform.
+
+    Returns:
+        numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
+    """
+
+    for _ in range(rounds - 1):
+        philox4_round(counter, key)
+
+        key[0] = key[0] + philox_w[0]
+        key[1] = key[1] + philox_w[1]
+
+    philox4_round(counter, key)
+    return counter
+
+
+def box_muller(x, y):
+    """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
+    u = x * two_pow32_inv + two_pow32_inv / 2
+    v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+
+    s = np.sqrt(-2.0 * np.log(u))
+
+    r1 = s * np.sin(v)
+    return r1.astype(np.float32)
+
+
+class Generator:
+    """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
+
+    def __init__(self, seed):
+        self.seed = seed
+        self.offset = 0
+
+    def randn(self, shape):
+        """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
+
+        n = 1
+        for x in shape:
+            n *= x
+
+        counter = np.zeros((4, n), dtype=np.uint32)
+        counter[0] = self.offset
+        counter[2] = np.arange(n, dtype=np.uint32)  # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
+        self.offset += 1
+
+        key = np.empty(n, dtype=np.uint64)
+        key.fill(self.seed)
+        key = uint32(key)
+
+        g = philox4_32(counter, key)
+
+        return box_muller(g[0], g[1]).reshape(shape)  # discard g[2] and g[3]

modules/script_loading.py

@@ -12,11 +12,12 @@ def load_module(path):
     return module
 
 
-def preload_extensions(extensions_dir, parser):
+def preload_extensions(extensions_dir, parser, extension_list=None):
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname in sorted(os.listdir(extensions_dir)):
+    extensions = extension_list if extension_list is not None else os.listdir(extensions_dir)
+    for dirname in sorted(extensions):
         preload_script = os.path.join(extensions_dir, dirname, "preload.py")
         if not os.path.isfile(preload_script):
             continue

modules/scripts.py

@@ -16,6 +16,11 @@ class PostprocessImageArgs:
         self.image = image
 
 
+class PostprocessBatchListArgs:
+    def __init__(self, images):
+        self.images = images
+
+
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -119,7 +124,7 @@ class Script:
 
     def after_extra_networks_activate(self, p, *args, **kwargs):
         """
-        Calledafter extra networks activation, before conds calculation
+        Called after extra networks activation, before conds calculation
         allow modification of the network after extra networks activation been applied
         won't be call if p.disable_extra_networks
 
@@ -156,6 +161,25 @@ class Script:
 
         pass
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+        """
+        Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+        This is useful when you want to update the entire batch instead of individual images.
+
+        You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+        If the number of images is different from the batch size when returning,
+        then the script has the responsibility to also update the following attributes in the processing object (p):
+          - p.prompts
+          - p.negative_prompts
+          - p.seeds
+          - p.subseeds
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+        """
+
+        pass
+
     def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
         """
         Called for every image after it has been generated.
@@ -536,6 +560,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+            except Exception:
+                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
             try:
@@ -599,49 +631,3 @@ def reload_script_body_only():
 
 
 reload_scripts = load_scripts  # compatibility alias
-
-
-def add_classes_to_gradio_component(comp):
-    """
-    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
-    """
-
-    comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
-
-    if getattr(comp, 'multiselect', False):
-        comp.elem_classes.append('multiselect')
-
-
-
-def IOComponent_init(self, *args, **kwargs):
-    if scripts_current is not None:
-        scripts_current.before_component(self, **kwargs)
-
-    script_callbacks.before_component_callback(self, **kwargs)
-
-    res = original_IOComponent_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    script_callbacks.after_component_callback(self, **kwargs)
-
-    if scripts_current is not None:
-        scripts_current.after_component(self, **kwargs)
-
-    return res
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-gr.components.IOComponent.__init__ = IOComponent_init
-
-
-def BlockContext_init(self, *args, **kwargs):
-    res = original_BlockContext_init(self, *args, **kwargs)
-
-    add_classes_to_gradio_component(self)
-
-    return res
-
-
-original_BlockContext_init = gr.blocks.BlockContext.__init__
-gr.blocks.BlockContext.__init__ = BlockContext_init

modules/sd_disable_initialization.py

@@ -3,8 +3,31 @@ import open_clip
 import torch
 import transformers.utils.hub
 
+from modules import shared
 
-class DisableInitialization:
+
+class ReplaceHelper:
+    def __init__(self):
+        self.replaced = []
+
+    def replace(self, obj, field, func):
+        original = getattr(obj, field, None)
+        if original is None:
+            return None
+
+        self.replaced.append((obj, field, original))
+        setattr(obj, field, func)
+
+        return original
+
+    def restore(self):
+        for obj, field, original in self.replaced:
+            setattr(obj, field, original)
+
+        self.replaced.clear()
+
+
+class DisableInitialization(ReplaceHelper):
     """
     When an object of this class enters a `with` block, it starts:
     - preventing torch's layer initialization functions from working
@@ -21,7 +44,7 @@ class DisableInitialization:
     """
 
     def __init__(self, disable_clip=True):
-        self.replaced = []
+        super().__init__()
         self.disable_clip = disable_clip
 
     def replace(self, obj, field, func):
@@ -86,8 +109,81 @@ class DisableInitialization:
             self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        for obj, field, original in self.replaced:
-            setattr(obj, field, original)
+        self.restore()
 
-        self.replaced.clear()
 
+class InitializeOnMeta(ReplaceHelper):
+    """
+    Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
+    which results in those parameters having no values and taking no memory. model.to() will be broken and
+    will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
+
+    Usage:
+    ```
+    with sd_disable_initialization.InitializeOnMeta():
+        sd_model = instantiate_from_config(sd_config.model)
+    ```
+    """
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        def set_device(x):
+            x["device"] = "meta"
+            return x
+
+        linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
+        conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
+        mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
+        self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()
+
+
+class LoadStateDictOnMeta(ReplaceHelper):
+    """
+    Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
+    As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
+    Meant to be used together with InitializeOnMeta above.
+
+    Usage:
+    ```
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
+        model.load_state_dict(state_dict, strict=False)
+    ```
+    """
+
+    def __init__(self, state_dict, device):
+        super().__init__()
+        self.state_dict = state_dict
+        self.device = device
+
+    def __enter__(self):
+        if shared.cmd_opts.disable_model_loading_ram_optimization:
+            return
+
+        sd = self.state_dict
+        device = self.device
+
+        def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
+            params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+
+            for name, param in params:
+                if param.is_meta:
+                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+
+            original(self, state_dict, prefix, *args, **kwargs)
+
+            for name, _ in params:
+                key = prefix + name
+                if key in sd:
+                    del sd[key]
+
+        linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
+        conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
+        mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.restore()

modules/sd_hijack.py

@@ -2,11 +2,10 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType
 
-import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -15,6 +14,11 @@ import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
 
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+import sgm.modules.diffusionmodules.openaimodel
+import sgm.modules.encoders.modules
+
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
@@ -25,8 +29,12 @@ ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.Cros
 ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
 
 # silence new console spam from SD2
-ldm.modules.attention.print = lambda *args: None
-ldm.modules.diffusionmodules.model.print = lambda *args: None
+ldm.modules.attention.print = shared.ldm_print
+ldm.modules.diffusionmodules.model.print = shared.ldm_print
+ldm.util.print = shared.ldm_print
+ldm.models.diffusion.ddpm.print = shared.ldm_print
+
+sd_hijack_inpainting.do_inpainting_hijack()
 
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
@@ -56,6 +64,9 @@ def apply_optimizations(option=None):
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
+    sgm.modules.diffusionmodules.model.nonlinearity = silu
+    sgm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+
     if current_optimizer is not None:
         current_optimizer.undo()
         current_optimizer = None
@@ -89,6 +100,10 @@ def undo_optimizations():
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
+    sgm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+    sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+    sgm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
 
 def fix_checkpoint():
     """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
@@ -152,12 +167,13 @@ class StableDiffusionModelHijack:
     clip = None
     optimization_method = None
 
-    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
-
     def __init__(self):
+        import modules.textual_inversion.textual_inversion
+
         self.extra_generation_params = {}
         self.comments = []
 
+        self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
     def apply_optimizations(self, option=None):
@@ -168,6 +184,32 @@ class StableDiffusionModelHijack:
             undo_optimizations()
 
     def hijack(self, m):
+        conditioner = getattr(m, 'conditioner', None)
+        if conditioner:
+            text_cond_models = []
+
+            for i in range(len(conditioner.embedders)):
+                embedder = conditioner.embedders[i]
+                typename = type(embedder).__name__
+                if typename == 'FrozenOpenCLIPEmbedder':
+                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
+                    conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(embedder, self)
+                    text_cond_models.append(conditioner.embedders[i])
+                if typename == 'FrozenCLIPEmbedder':
+                    model_embeddings = embedder.transformer.text_model.embeddings
+                    model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+                    conditioner.embedders[i] = sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords(embedder, self)
+                    text_cond_models.append(conditioner.embedders[i])
+                if typename == 'FrozenOpenCLIPEmbedder2':
+                    embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self, textual_inversion_key='clip_g')
+                    conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords(embedder, self)
+                    text_cond_models.append(conditioner.embedders[i])
+
+            if len(text_cond_models) == 1:
+                m.cond_stage_model = text_cond_models[0]
+            else:
+                m.cond_stage_model = conditioner
+
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
@@ -205,7 +247,7 @@ class StableDiffusionModelHijack:
         ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
@@ -254,10 +296,11 @@ class StableDiffusionModelHijack:
 
 
 class EmbeddingsWithFixes(torch.nn.Module):
-    def __init__(self, wrapped, embeddings):
+    def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
         super().__init__()
         self.wrapped = wrapped
         self.embeddings = embeddings
+        self.textual_inversion_key = textual_inversion_key
 
     def forward(self, input_ids):
         batch_fixes = self.embeddings.fixes
@@ -271,7 +314,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
         vecs = []
         for fixes, tensor in zip(batch_fixes, inputs_embeds):
             for offset, embedding in fixes:
-                emb = devices.cond_cast_unet(embedding.vec)
+                vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+                emb = devices.cond_cast_unet(vec)
                 emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                 tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
 

modules/sd_hijack_clip.py

@@ -42,6 +42,10 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
         self.chunk_length = 75
 
+        self.is_trainable = getattr(wrapped, 'is_trainable', False)
+        self.input_key = getattr(wrapped, 'input_key', 'txt')
+        self.legacy_ucg_val = None
+
     def empty_chunk(self):
         """creates an empty PromptChunk and returns it"""
 
@@ -157,7 +161,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     position += 1
                     continue
 
-                emb_len = int(embedding.vec.shape[0])
+                emb_len = int(embedding.vectors)
                 if len(chunk.tokens) + emb_len > self.chunk_length:
                     next_chunk()
 
@@ -199,8 +203,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         """
         Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
         Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
-        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.
+        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
         An example shape returned by this function can be: (2, 77, 768).
+        For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
         Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
         is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
         """
@@ -240,9 +245,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                 hashes.append(f"{name}: {shorthash}")
 
             if hashes:
+                if self.hijack.extra_generation_params.get("TI hashes"):
+                    hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
                 self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
-        return torch.hstack(zs)
+        if getattr(self.wrapped, 'return_pooled', False):
+            return torch.hstack(zs), zs[0].pooled
+        else:
+            return torch.hstack(zs)
 
     def process_tokens(self, remade_batch_tokens, batch_multipliers):
         """
@@ -262,6 +272,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
         z = self.encode_with_transformers(tokens)
 
+        pooled = getattr(z, 'pooled', None)
+
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
@@ -269,6 +281,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         new_mean = z.mean()
         z = z * (original_mean / new_mean)
 
+        if pooled is not None:
+            z.pooled = pooled
+
         return z
 
 
@@ -324,3 +339,18 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
         embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
 
         return embedded
+
+
+class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+    def encode_with_transformers(self, tokens):
+        outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
+
+        if self.wrapped.layer == "last":
+            z = outputs.last_hidden_state
+        else:
+            z = outputs.hidden_states[self.wrapped.layer_idx]
+
+        return z

modules/sd_hijack_inpainting.py

@@ -92,6 +92,4 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
 
 def do_inpainting_hijack():
-    # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms

modules/sd_hijack_open_clip.py

@@ -35,3 +35,37 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
         embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
 
         return embedded
+
+
+class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+        self.id_start = tokenizer.encoder["<start_of_text>"]
+        self.id_end = tokenizer.encoder["<end_of_text>"]
+        self.id_pad = 0
+
+    def tokenize(self, texts):
+        assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+        tokenized = [tokenizer.encode(text) for text in texts]
+
+        return tokenized
+
+    def encode_with_transformers(self, tokens):
+        d = self.wrapped.encode_with_transformer(tokens)
+        z = d[self.wrapped.layer]
+
+        pooled = d.get("pooled")
+        if pooled is not None:
+            z.pooled = pooled
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        ids = tokenizer.encode(init_text)
+        ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+
+        return embedded

modules/sd_hijack_optimizations.py

@@ -14,7 +14,11 @@ from modules.hypernetworks import hypernetwork
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+sgm_diffusionmodules_model_AttnBlock_forward = sgm.modules.diffusionmodules.model.AttnBlock.forward
 
 
 class SdOptimization:
@@ -39,6 +43,9 @@ class SdOptimization:
         ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
+        sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = sgm_diffusionmodules_model_AttnBlock_forward
+
 
 class SdOptimizationXformers(SdOptimization):
     name = "xformers"
@@ -51,6 +58,8 @@ class SdOptimizationXformers(SdOptimization):
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+        sgm.modules.attention.CrossAttention.forward = xformers_attention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
 
 
 class SdOptimizationSdpNoMem(SdOptimization):
@@ -65,6 +74,8 @@ class SdOptimizationSdpNoMem(SdOptimization):
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+        sgm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
 
 
 class SdOptimizationSdp(SdOptimizationSdpNoMem):
@@ -76,6 +87,8 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+        sgm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
 
 
 class SdOptimizationSubQuad(SdOptimization):
@@ -86,6 +99,8 @@ class SdOptimizationSubQuad(SdOptimization):
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+        sgm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
 
 
 class SdOptimizationV1(SdOptimization):
@@ -94,9 +109,9 @@ class SdOptimizationV1(SdOptimization):
     cmd_opt = "opt_split_attention_v1"
     priority = 10
 
-
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
 
 
 class SdOptimizationInvokeAI(SdOptimization):
@@ -109,6 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization):
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
 
 
 class SdOptimizationDoggettx(SdOptimization):
@@ -119,6 +135,8 @@ class SdOptimizationDoggettx(SdOptimization):
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+        sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+        sgm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
 
 
 def list_optimizers(res):
@@ -155,7 +173,7 @@ def get_available_vram():
 
 
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
-def split_cross_attention_forward_v1(self, x, context=None, mask=None):
+def split_cross_attention_forward_v1(self, x, context=None, mask=None, **kwargs):
     h = self.heads
 
     q_in = self.to_q(x)
@@ -196,7 +214,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
 
 
 # taken from https://github.com/Doggettx/stable-diffusion and modified
-def split_cross_attention_forward(self, x, context=None, mask=None):
+def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
     h = self.heads
 
     q_in = self.to_q(x)
@@ -238,9 +256,9 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
             raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                                f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
 
-        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        slice_size = q.shape[1] // steps
         for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
+            end = min(i + slice_size, q.shape[1])
             s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
 
             s2 = s1.softmax(dim=-1, dtype=q.dtype)
@@ -262,11 +280,13 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 # -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
 mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
+
 def einsum_op_compvis(q, k, v):
     s = einsum('b i d, b j d -> b i j', q, k)
     s = s.softmax(dim=-1, dtype=s.dtype)
     return einsum('b i j, b j d -> b i d', s, v)
 
+
 def einsum_op_slice_0(q, k, v, slice_size):
     r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
     for i in range(0, q.shape[0], slice_size):
@@ -274,6 +294,7 @@ def einsum_op_slice_0(q, k, v, slice_size):
         r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
     return r
 
+
 def einsum_op_slice_1(q, k, v, slice_size):
     r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
     for i in range(0, q.shape[1], slice_size):
@@ -281,6 +302,7 @@ def einsum_op_slice_1(q, k, v, slice_size):
         r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
     return r
 
+
 def einsum_op_mps_v1(q, k, v):
     if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
         return einsum_op_compvis(q, k, v)
@@ -290,12 +312,14 @@ def einsum_op_mps_v1(q, k, v):
             slice_size -= 1
         return einsum_op_slice_1(q, k, v, slice_size)
 
+
 def einsum_op_mps_v2(q, k, v):
     if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
         return einsum_op_compvis(q, k, v)
     else:
         return einsum_op_slice_0(q, k, v, 1)
 
+
 def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
     size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
     if size_mb <= max_tensor_mb:
@@ -305,6 +329,7 @@ def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
         return einsum_op_slice_0(q, k, v, q.shape[0] // div)
     return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
 
+
 def einsum_op_cuda(q, k, v):
     stats = torch.cuda.memory_stats(q.device)
     mem_active = stats['active_bytes.all.current']
@@ -315,6 +340,7 @@ def einsum_op_cuda(q, k, v):
     # Divide factor of safety as there's copying and fragmentation
     return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
+
 def einsum_op(q, k, v):
     if q.device.type == 'cuda':
         return einsum_op_cuda(q, k, v)
@@ -328,7 +354,8 @@ def einsum_op(q, k, v):
     # Tested on i7 with 8MB L3 cache.
     return einsum_op_tensor_mem(q, k, v, 32)
 
-def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None, **kwargs):
     h = self.heads
 
     q = self.to_q(x)
@@ -356,7 +383,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
 
 # Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
 # The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
-def sub_quad_attention_forward(self, x, context=None, mask=None):
+def sub_quad_attention_forward(self, x, context=None, mask=None, **kwargs):
     assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
 
     h = self.heads
@@ -392,6 +419,7 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
 
     return x
 
+
 def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
     bytes_per_token = torch.finfo(q.dtype).bits//8
     batch_x_heads, q_tokens, _ = q.shape
@@ -442,7 +470,7 @@ def get_xformers_flash_attention_op(q, k, v):
     return None
 
 
-def xformers_attention_forward(self, x, context=None, mask=None):
+def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
@@ -465,9 +493,10 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     return self.to_out(out)
 
+
 # Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
 # The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
-def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None, **kwargs):
     batch_size, sequence_length, inner_dim = x.shape
 
     if mask is not None:
@@ -507,10 +536,12 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
     hidden_states = self.to_out[1](hidden_states)
     return hidden_states
 
-def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None, **kwargs):
     with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
         return scaled_dot_product_attention_forward(self, x, context, mask)
 
+
 def cross_attention_attnblock_forward(self, x):
         h_ = x
         h_ = self.norm(h_)
@@ -569,6 +600,7 @@ def cross_attention_attnblock_forward(self, x):
 
         return h3
 
+
 def xformers_attnblock_forward(self, x):
     try:
         h_ = x
@@ -592,6 +624,7 @@ def xformers_attnblock_forward(self, x):
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
 
+
 def sdp_attnblock_forward(self, x):
     h_ = x
     h_ = self.norm(h_)
@@ -612,10 +645,12 @@ def sdp_attnblock_forward(self, x):
     out = self.proj_out(out)
     return x + out
 
+
 def sdp_no_mem_attnblock_forward(self, x):
     with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
         return sdp_attnblock_forward(self, x)
 
+
 def sub_quad_attnblock_forward(self, x):
     h_ = x
     h_ = self.norm(h_)

modules/sd_hijack_unet.py

@@ -39,7 +39,10 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
 
     if isinstance(cond, dict):
         for y in cond.keys():
-            cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+            if isinstance(cond[y], list):
+                cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+            else:
+                cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
 
     with devices.autocast():
         return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
@@ -77,3 +80,6 @@ first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devi
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)

modules/sd_models.py

@@ -14,8 +14,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet
-from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
 from modules.timer import Timer
 import tomesd
 
@@ -33,6 +32,8 @@ class CheckpointInfo:
         self.filename = filename
         abspath = os.path.abspath(filename)
 
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
         if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
             name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
         elif abspath.startswith(model_path):
@@ -43,6 +44,19 @@ class CheckpointInfo:
         if name.startswith("\\") or name.startswith("/"):
             name = name[1:]
 
+        def read_metadata():
+            metadata = read_metadata_from_safetensors(filename)
+            self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
+
+            return metadata
+
+        self.metadata = {}
+        if self.is_safetensors:
+            try:
+                self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
+            except Exception as e:
+                errors.display(e, f"reading metadata for {filename}")
+
         self.name = name
         self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
         self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
@@ -52,17 +66,9 @@ class CheckpointInfo:
         self.shorthash = self.sha256[0:10] if self.sha256 else None
 
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+        self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
 
-        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
-
-        self.metadata = {}
-
-        _, ext = os.path.splitext(self.filename)
-        if ext.lower() == ".safetensors":
-            try:
-                self.metadata = read_metadata_from_safetensors(filename)
-            except Exception as e:
-                errors.display(e, f"reading checkpoint metadata: {filename}")
+        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
     def register(self):
         checkpoints_list[self.title] = self
@@ -79,8 +85,9 @@ class CheckpointInfo:
         if self.shorthash not in self.ids:
             self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
 
-        checkpoints_list.pop(self.title)
+        checkpoints_list.pop(self.title, None)
         self.title = f'{self.name} [{self.shorthash}]'
+        self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
         self.register()
 
         return self.shorthash
@@ -101,14 +108,8 @@ def setup_model():
     enable_midas_autodownload()
 
 
-def checkpoint_tiles():
-    def convert(name):
-        return int(name) if name.isdigit() else name.lower()
-
-    def alphanumeric_key(key):
-        return [convert(c) for c in re.split('([0-9]+)', key)]
-
-    return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)
+def checkpoint_tiles(use_short=False):
+    return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
 
 
 def list_models():
@@ -131,11 +132,14 @@ def list_models():
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
 
-    for filename in sorted(model_list, key=str.lower):
+    for filename in model_list:
         checkpoint_info = CheckpointInfo(filename)
         checkpoint_info.register()
 
 
+re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
+
+
 def get_closet_checkpoint_match(search_string):
     checkpoint_info = checkpoint_aliases.get(search_string, None)
     if checkpoint_info is not None:
@@ -145,6 +149,11 @@ def get_closet_checkpoint_match(search_string):
     if found:
         return found[0]
 
+    search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
+    found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
+    if found:
+        return found[0]
+
     return None
 
 
@@ -289,13 +298,21 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     if state_dict is None:
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
+    model.is_sdxl = hasattr(model, 'conditioner')
+    model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
+    model.is_sd1 = not model.is_sdxl and not model.is_sd2
+
+    if model.is_sdxl:
+        sd_models_xl.extend_sdxl(model)
+
     model.load_state_dict(state_dict, strict=False)
-    del state_dict
     timer.record("apply weights to model")
 
     if shared.opts.sd_checkpoint_cache > 0:
         # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+        checkpoints_loaded[checkpoint_info] = state_dict
+
+    del state_dict
 
     if shared.cmd_opts.opt_channelslast:
         model.to(memory_format=torch.channels_last)
@@ -319,7 +336,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
         timer.record("apply half()")
 
-    devices.dtype_unet = model.model.diffusion_model.dtype
+    devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
     devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
 
     model.first_stage_model.to(devices.dtype_vae)
@@ -334,7 +351,8 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     model.sd_checkpoint_info = checkpoint_info
     shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
 
-    model.logvar = model.logvar.to(devices.device)  # fix for training
+    if hasattr(model, 'logvar'):
+        model.logvar = model.logvar.to(devices.device)  # fix for training
 
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
@@ -391,10 +409,11 @@ def repair_config(sd_config):
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
-    if shared.cmd_opts.no_half:
-        sd_config.model.params.unet_config.params.use_fp16 = False
-    elif shared.cmd_opts.upcast_sampling:
-        sd_config.model.params.unet_config.params.use_fp16 = True
+    if hasattr(sd_config.model.params, 'unet_config'):
+        if shared.cmd_opts.no_half:
+            sd_config.model.params.unet_config.params.use_fp16 = False
+        elif shared.cmd_opts.upcast_sampling:
+            sd_config.model.params.unet_config.params.use_fp16 = True
 
     if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
         sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
@@ -407,11 +426,14 @@ def repair_config(sd_config):
 
 sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
 sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
+sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
+sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
 
 
 class SdModelData:
     def __init__(self):
         self.sd_model = None
+        self.loaded_sd_models = []
         self.was_loaded_at_least_once = False
         self.lock = threading.Lock()
 
@@ -426,6 +448,7 @@ class SdModelData:
 
                 try:
                     load_model()
+
                 except Exception as e:
                     errors.display(e, "loading stable diffusion model", full_traceback=True)
                     print("", file=sys.stderr)
@@ -437,23 +460,68 @@ class SdModelData:
     def set_sd_model(self, v):
         self.sd_model = v
 
+        try:
+            self.loaded_sd_models.remove(v)
+        except ValueError:
+            pass
+
+        if v is not None:
+            self.loaded_sd_models.insert(0, v)
+
 
 model_data = SdModelData()
 
 
+def get_empty_cond(sd_model):
+    from modules import extra_networks, processing
+
+    p = processing.StableDiffusionProcessingTxt2Img()
+    extra_networks.activate(p, {})
+
+    if hasattr(sd_model, 'conditioner'):
+        d = sd_model.get_learned_conditioning([""])
+        return d['crossattn']
+    else:
+        return sd_model.cond_stage_model([""])
+
+
+def send_model_to_cpu(m):
+    from modules import lowvram
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.send_everything_to_cpu()
+    else:
+        m.to(devices.cpu)
+
+    devices.torch_gc()
+
+
+def send_model_to_device(m):
+    from modules import lowvram
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
+    else:
+        m.to(shared.device)
+
+
+def send_model_to_trash(m):
+    m.to(device="meta")
+    devices.torch_gc()
+
+
 def load_model(checkpoint_info=None, already_loaded_state_dict=None):
-    from modules import lowvram, sd_hijack
+    from modules import sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
 
+    timer = Timer()
+
     if model_data.sd_model:
-        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+        send_model_to_trash(model_data.sd_model)
         model_data.sd_model = None
-        gc.collect()
         devices.torch_gc()
 
-    do_inpainting_hijack()
-
-    timer = Timer()
+    timer.record("unload existing model")
 
     if already_loaded_state_dict is not None:
         state_dict = already_loaded_state_dict
@@ -461,7 +529,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
         state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
     checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
-    clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
+    clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
 
     timer.record("find config")
 
@@ -474,26 +542,28 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     sd_model = None
     try:
-        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
-            sd_model = instantiate_from_config(sd_config.model)
-    except Exception:
-        pass
+        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
+            with sd_disable_initialization.InitializeOnMeta():
+                sd_model = instantiate_from_config(sd_config.model)
+
+    except Exception as e:
+        errors.display(e, "creating model quickly", full_traceback=True)
 
     if sd_model is None:
         print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
-        sd_model = instantiate_from_config(sd_config.model)
+
+        with sd_disable_initialization.InitializeOnMeta():
+            sd_model = instantiate_from_config(sd_config.model)
 
     sd_model.used_config = checkpoint_config
 
     timer.record("create model")
 
-    load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
-    else:
-        sd_model.to(shared.device)
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
+        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+    timer.record("load weights from state dict")
 
+    send_model_to_device(sd_model)
     timer.record("move model to device")
 
     sd_hijack.model_hijack.hijack(sd_model)
@@ -501,7 +571,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     timer.record("hijack")
 
     sd_model.eval()
-    model_data.sd_model = sd_model
+    model_data.set_sd_model(sd_model)
     model_data.was_loaded_at_least_once = True
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
@@ -513,7 +583,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     timer.record("scripts callbacks")
 
     with devices.autocast(), torch.no_grad():
-        sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+        sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
 
     timer.record("calculate empty prompt")
 
@@ -522,10 +592,61 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     return sd_model
 
 
+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+    """
+    Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
+    If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
+    If not, returns the model that can be used to load weights from checkpoint_info's file.
+    If no such model exists, returns None.
+    Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+    """
+
+    already_loaded = None
+    for i in reversed(range(len(model_data.loaded_sd_models))):
+        loaded_model = model_data.loaded_sd_models[i]
+        if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+            already_loaded = loaded_model
+            continue
+
+        if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
+            print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
+            model_data.loaded_sd_models.pop()
+            send_model_to_trash(loaded_model)
+            timer.record("send model to trash")
+
+        if shared.opts.sd_checkpoints_keep_in_cpu:
+            send_model_to_cpu(sd_model)
+            timer.record("send model to cpu")
+
+    if already_loaded is not None:
+        send_model_to_device(already_loaded)
+        timer.record("send model to device")
+
+        model_data.set_sd_model(already_loaded)
+        print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
+        return model_data.sd_model
+    elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
+        print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
+
+        model_data.sd_model = None
+        load_model(checkpoint_info)
+        return model_data.sd_model
+    elif len(model_data.loaded_sd_models) > 0:
+        sd_model = model_data.loaded_sd_models.pop()
+        model_data.sd_model = sd_model
+
+        print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
+        return sd_model
+    else:
+        return None
+
+
 def reload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
+    from modules import devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
+    timer = Timer()
+
     if not sd_model:
         sd_model = model_data.sd_model
 
@@ -534,19 +655,17 @@ def reload_model_weights(sd_model=None, info=None):
     else:
         current_checkpoint_info = sd_model.sd_checkpoint_info
         if sd_model.sd_model_checkpoint == checkpoint_info.filename:
-            return
+            return sd_model
 
+    sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
+    if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+        return sd_model
+
+    if sd_model is not None:
         sd_unet.apply_unet("None")
-
-        if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-            lowvram.send_everything_to_cpu()
-        else:
-            sd_model.to(devices.cpu)
-
+        send_model_to_cpu(sd_model)
         sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    timer = Timer()
-
     state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
 
     checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
@@ -554,7 +673,9 @@ def reload_model_weights(sd_model=None, info=None):
     timer.record("find config")
 
     if sd_model is None or checkpoint_config != sd_model.used_config:
-        del sd_model
+        if sd_model is not None:
+            send_model_to_trash(sd_model)
+
         load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         return model_data.sd_model
 
@@ -577,6 +698,8 @@ def reload_model_weights(sd_model=None, info=None):
 
     print(f"Weights loaded in {timer.summary()}.")
 
+    model_data.set_sd_model(sd_model)
+
     return sd_model
 
 

modules/sd_models_config.py

@@ -6,12 +6,15 @@ from modules import shared, paths, sd_disable_initialization
 
 sd_configs_path = shared.sd_configs_path
 sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
 
 
 config_default = shared.sd_default_config
 config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
 config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
 config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
+config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
 config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
 config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
 config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
@@ -68,7 +71,11 @@ def guess_model_config_from_state_dict(sd, filename):
     diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
     sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
 
-    if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+    if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
+        return config_sdxl
+    if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
+        return config_sdxl_refiner
+    elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
         return config_depth_model
     elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
         return config_unclip

modules/sd_models_xl.py

@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import torch
+
+import sgm.models.diffusion
+import sgm.modules.diffusionmodules.denoiser_scaling
+import sgm.modules.diffusionmodules.discretizer
+from modules import devices, shared, prompt_parser
+
+
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
+    for embedder in self.conditioner.embedders:
+        embedder.ucg_rate = 0.0
+
+    width = getattr(batch, 'width', 1024)
+    height = getattr(batch, 'height', 1024)
+    is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
+    aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
+
+    devices_args = dict(device=devices.device, dtype=devices.dtype)
+
+    sdxl_conds = {
+        "txt": batch,
+        "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+        "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
+        "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+        "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
+    }
+
+    force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
+    c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
+
+    return c
+
+
+def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+    return self.model(x, t, cond)
+
+
+def get_first_stage_encoding(self, x):  # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
+    return x
+
+
+sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
+sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
+sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
+
+
+def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
+    res = []
+
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
+        encoded = embedder.encode_embedding_init_text(init_text, nvpt)
+        res.append(encoded)
+
+    return torch.cat(res, dim=1)
+
+
+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+        return embedder.tokenize(texts)
+
+    raise AssertionError('no tokenizer available')
+
+
+
+def process_texts(self, texts):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
+        return embedder.process_texts(texts)
+
+
+def get_target_prompt_token_count(self, token_count):
+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
+        return embedder.get_target_prompt_token_count(token_count)
+
+
+# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
+sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
+sgm.modules.GeneralConditioner.process_texts = process_texts
+sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
+
+
+def extend_sdxl(model):
+    """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
+
+    dtype = next(model.model.diffusion_model.parameters()).dtype
+    model.model.diffusion_model.dtype = dtype
+    model.model.conditioning_key = 'crossattn'
+    model.cond_stage_key = 'txt'
+    # model.cond_stage_model will be set in sd_hijack
+
+    model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
+
+    discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
+    model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+
+    model.conditioner.wrapped = torch.nn.Module()
+
+
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print
+
+# this gets the code to load the vanilla attention that we override
+sgm.modules.attention.SDP_IS_AVAILABLE = True
+sgm.modules.attention.XFORMERS_IS_AVAILABLE = False

modules/sd_samplers.py

@@ -28,6 +28,9 @@ def create_sampler(name, model):
 
     assert config is not None, f'bad sampler name: {name}'
 
+    if model.is_sdxl and config.options.get("no_sdxl", False):
+        raise Exception(f"Sampler {config.name} is not supported for SDXL")
+
     sampler = config.constructor(model)
     sampler.config = config
 

modules/sd_samplers_common.py

@@ -2,10 +2,8 @@ from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
-
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
 from modules.shared import opts, state
-import modules.shared as shared
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
@@ -37,7 +35,7 @@ def single_sample_to_image(sample, approximation=None):
         x_sample = sample * 1.5
         x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
     else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
+        x_sample = decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
 
     x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
@@ -46,6 +44,12 @@ def single_sample_to_image(sample, approximation=None):
     return Image.fromarray(x_sample)
 
 
+def decode_first_stage(model, x):
+    x = model.decode_first_stage(x.to(devices.dtype_vae))
+
+    return x
+
+
 def sample_to_image(samples, index=0, approximation=None):
     return single_sample_to_image(samples[index], approximation)
 
@@ -85,11 +89,13 @@ class InterruptedException(BaseException):
     pass
 
 
-if opts.randn_source == "CPU":
+def replace_torchsde_browinan():
     import torchsde._brownian.brownian_interval
 
     def torchsde_randn(size, dtype, device, seed):
-        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
-        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+        return devices.randn_local(seed, size).to(device=device, dtype=dtype)
 
     torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+replace_torchsde_browinan()

modules/sd_samplers_compvis.py

@@ -11,9 +11,9 @@ import modules.models.diffusion.uni_pc
 
 
 samplers_data_compvis = [
-    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True}),
-    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
-    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
+    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),
+    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {"no_sdxl": True}),
+    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {"no_sdxl": True}),
 ]
 
 

modules/sd_samplers_extra.py

@@ -0,0 +1,74 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+    Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+    If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+    """
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    step_id = 0
+    from k_diffusion.sampling import to_d, get_sigmas_karras
+
+    def heun_step(x, old_sigma, new_sigma, second_order=True):
+        nonlocal step_id
+        denoised = model(x, old_sigma * s_in, **extra_args)
+        d = to_d(x, old_sigma, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+        dt = new_sigma - old_sigma
+        if new_sigma == 0 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+            restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+        else:
+            restart_list = {}
+
+    restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
+    step_list = []
+    for i in range(len(sigmas) - 1):
+        step_list.append((sigmas[i], sigmas[i + 1]))
+        if i + 1 in restart_list:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+                while restart_times > 0:
+                    restart_times -= 1
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+
+    last_sigma = None
+    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
+        if last_sigma is None:
+            last_sigma = old_sigma
+        elif last_sigma < old_sigma:
+            x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+        x = heun_step(x, old_sigma, new_sigma)
+        last_sigma = new_sigma
+
+    return x

modules/sd_samplers_kdiffusion.py

@@ -2,7 +2,7 @@ from collections import deque
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
 
 from modules.shared import opts, state
 import modules.shared as shared
@@ -30,12 +30,15 @@ samplers_k_diffusion = [
     ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
     ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
     ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
 ]
 
+
 samplers_data_k_diffusion = [
     sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
     for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
+    if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
 ]
 
 sampler_extra_params = {
@@ -53,6 +56,28 @@ k_diffusion_scheduler = {
 }
 
 
+def catenate_conds(conds):
+    if not isinstance(conds[0], dict):
+        return torch.cat(conds)
+
+    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+    if not isinstance(cond, dict):
+        return cond[a:b]
+
+    return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+    if not isinstance(tensor, dict):
+        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+    return tensor
+
+
 class CFGDenoiser(torch.nn.Module):
     """
     Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
@@ -105,10 +130,13 @@ class CFGDenoiser(torch.nn.Module):
 
         if shared.sd_model.model.conditioning_key == "crossattn-adm":
             image_uncond = torch.zeros_like(image_cond)
-            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
         else:
             image_uncond = image_cond
-            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+            if isinstance(uncond, dict):
+                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+            else:
+                make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
 
         if not is_edit_model:
             x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
@@ -140,28 +168,28 @@ class CFGDenoiser(torch.nn.Module):
             num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
 
             if num_repeats < 0:
-                tensor = torch.cat([tensor, empty.repeat((tensor.shape[0], -num_repeats, 1))], axis=1)
+                tensor = pad_cond(tensor, -num_repeats, empty)
                 self.padded_cond_uncond = True
             elif num_repeats > 0:
-                uncond = torch.cat([uncond, empty.repeat((uncond.shape[0], num_repeats, 1))], axis=1)
+                uncond = pad_cond(uncond, num_repeats, empty)
                 self.padded_cond_uncond = True
 
         if tensor.shape[1] == uncond.shape[1] or skip_uncond:
             if is_edit_model:
-                cond_in = torch.cat([tensor, uncond, uncond])
+                cond_in = catenate_conds([tensor, uncond, uncond])
             elif skip_uncond:
                 cond_in = tensor
             else:
-                cond_in = torch.cat([tensor, uncond])
+                cond_in = catenate_conds([tensor, uncond])
 
             if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
             else:
                 x_out = torch.zeros_like(x_in)
                 for batch_offset in range(0, x_out.shape[0], batch_size):
                     a = batch_offset
                     b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
         else:
             x_out = torch.zeros_like(x_in)
             batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -170,14 +198,14 @@ class CFGDenoiser(torch.nn.Module):
                 b = min(a + batch_size, tensor.shape[0])
 
                 if not is_edit_model:
-                    c_crossattn = [tensor[a:b]]
+                    c_crossattn = subscript_cond(tensor, a, b)
                 else:
                     c_crossattn = torch.cat([tensor[a:b]], uncond)
 
                 x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
 
             if not skip_uncond:
-                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
 
         denoised_image_indexes = [x[0][0] for x in conds_list]
         if skip_uncond:
@@ -233,10 +261,7 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        if opts.randn_source == "CPU" or x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
+        return devices.randn_like(x)
 
 
 class KDiffusionSampler:
@@ -245,7 +270,7 @@ class KDiffusionSampler:
 
         self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
         self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
@@ -351,6 +376,9 @@ class KDiffusionSampler:
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
 
             sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+            m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+            sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 

modules/sd_vae.py

@@ -1,6 +1,6 @@
 import os
 import collections
-from modules import paths, shared, devices, script_callbacks, sd_models
+from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks
 import glob
 from copy import deepcopy
 
@@ -16,6 +16,7 @@ checkpoint_info = None
 
 checkpoints_loaded = collections.OrderedDict()
 
+
 def get_base_vae(model):
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
         return base_vae
@@ -50,6 +51,7 @@ def get_filename(filepath):
 
 
 def refresh_vae_list():
+    global vae_dict
     vae_dict.clear()
 
     paths = [
@@ -83,6 +85,8 @@ def refresh_vae_list():
         name = get_filename(filepath)
         vae_dict[name] = filepath
 
+    vae_dict = dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0])))
+
 
 def find_vae_near_checkpoint(checkpoint_file):
     checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0]
@@ -97,6 +101,16 @@ def resolve_vae(checkpoint_file):
     if shared.cmd_opts.vae_path is not None:
         return shared.cmd_opts.vae_path, 'from commandline argument'
 
+    metadata = extra_networks.get_user_metadata(checkpoint_file)
+    vae_metadata = metadata.get("vae", None)
+    if vae_metadata is not None and vae_metadata != "Automatic":
+        if vae_metadata == "None":
+            return None, None
+
+        vae_from_metadata = vae_dict.get(vae_metadata, None)
+        if vae_from_metadata is not None:
+            return vae_from_metadata, "from user metadata"
+
     is_automatic = shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
 
     vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)

modules/sd_vae_approx.py

@@ -2,9 +2,9 @@ import os
 
 import torch
 from torch import nn
-from modules import devices, paths
+from modules import devices, paths, shared
 
-sd_vae_approx_model = None
+sd_vae_approx_models = {}
 
 
 class VAEApprox(nn.Module):
@@ -31,30 +31,55 @@ class VAEApprox(nn.Module):
         return x
 
 
+def download_model(model_path, model_url):
+    if not os.path.exists(model_path):
+        os.makedirs(os.path.dirname(model_path), exist_ok=True)
+
+        print(f'Downloading VAEApprox model to: {model_path}')
+        torch.hub.download_url_to_file(model_url, model_path)
+
+
 def model():
-    global sd_vae_approx_model
+    model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
+    loaded_model = sd_vae_approx_models.get(model_name)
 
-    if sd_vae_approx_model is None:
-        model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
-        sd_vae_approx_model = VAEApprox()
+    if loaded_model is None:
+        model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
         if not os.path.exists(model_path):
-            model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
-        sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
-        sd_vae_approx_model.eval()
-        sd_vae_approx_model.to(devices.device, devices.dtype)
+            model_path = os.path.join(paths.script_path, "models", "VAE-approx", model_name)
 
-    return sd_vae_approx_model
+        if not os.path.exists(model_path):
+            model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
+            download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name)
+
+        loaded_model = VAEApprox()
+        loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+        loaded_model.eval()
+        loaded_model.to(devices.device, devices.dtype)
+        sd_vae_approx_models[model_name] = loaded_model
+
+    return loaded_model
 
 
 def cheap_approximation(sample):
     # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
 
-    coefs = torch.tensor([
-        [0.298, 0.207, 0.208],
-        [0.187, 0.286, 0.173],
-        [-0.158, 0.189, 0.264],
-        [-0.184, -0.271, -0.473],
-    ]).to(sample.device)
+    if shared.sd_model.is_sdxl:
+        coeffs = [
+            [ 0.3448,  0.4168,  0.4395],
+            [-0.1953, -0.0290,  0.0250],
+            [ 0.1074,  0.0886, -0.0163],
+            [-0.3730, -0.2499, -0.2088],
+        ]
+    else:
+        coeffs = [
+            [ 0.298,  0.207,  0.208],
+            [ 0.187,  0.286,  0.173],
+            [-0.158,  0.189,  0.264],
+            [-0.184, -0.271, -0.473],
+        ]
+
+    coefs = torch.tensor(coeffs).to(sample.device)
 
     x_sample = torch.einsum("lxy,lr -> rxy", sample, coefs)
 

modules/sd_vae_taesd.py

@@ -8,9 +8,9 @@ import os
 import torch
 import torch.nn as nn
 
-from modules import devices, paths_internal
+from modules import devices, paths_internal, shared
 
-sd_vae_taesd = None
+sd_vae_taesd_models = {}
 
 
 def conv(n_in, n_out, **kwargs):
@@ -61,9 +61,7 @@ class TAESD(nn.Module):
         return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
 
 
-def download_model(model_path):
-    model_url = 'https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth'
-
+def download_model(model_path, model_url):
     if not os.path.exists(model_path):
         os.makedirs(os.path.dirname(model_path), exist_ok=True)
 
@@ -72,17 +70,19 @@ def download_model(model_path):
 
 
 def model():
-    global sd_vae_taesd
+    model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
+    loaded_model = sd_vae_taesd_models.get(model_name)
 
-    if sd_vae_taesd is None:
-        model_path = os.path.join(paths_internal.models_path, "VAE-taesd", "taesd_decoder.pth")
-        download_model(model_path)
+    if loaded_model is None:
+        model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
+        download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
 
         if os.path.exists(model_path):
-            sd_vae_taesd = TAESD(model_path)
-            sd_vae_taesd.eval()
-            sd_vae_taesd.to(devices.device, devices.dtype)
+            loaded_model = TAESD(model_path)
+            loaded_model.eval()
+            loaded_model.to(devices.device, devices.dtype)
+            sd_vae_taesd_models[model_name] = loaded_model
         else:
             raise FileNotFoundError('TAESD model not found')
 
-    return sd_vae_taesd.decoder
+    return loaded_model.decoder

modules/shared.py

@@ -11,6 +11,7 @@ import gradio as gr
 import torch
 import tqdm
 
+import launch
 import modules.interrogate
 import modules.memmon
 import modules.styles
@@ -26,7 +27,7 @@ demo = None
 
 parser = cmd_args.parser
 
-script_loading.preload_extensions(extensions_dir, parser)
+script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
 script_loading.preload_extensions(extensions_builtin_dir, parser)
 
 if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
@@ -219,12 +220,19 @@ class State:
             return
 
         import modules.sd_samplers
-        if opts.show_progress_grid:
-            self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
-        else:
-            self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
 
-        self.current_image_sampling_step = self.sampling_step
+        try:
+            if opts.show_progress_grid:
+                self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
+            else:
+                self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
+
+            self.current_image_sampling_step = self.sampling_step
+
+        except Exception:
+            # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+            # we silently ignore this error
+            errors.record_exception()
 
     def assign_current_image(self, image):
         self.current_image = image
@@ -384,13 +392,15 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
 }))
 
 options_templates.update(options_section(('system', "System"), {
-    "show_warnings": OptionInfo(False, "Show warnings in console."),
+    "show_warnings": OptionInfo(False, "Show warnings in console.").needs_restart(),
+    "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_restart(),
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
     "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
     "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
     "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
     "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
+    "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
 }))
 
 options_templates.update(options_section(('training', "Training"), {
@@ -410,28 +420,49 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
-    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
+    "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
     "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
     "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
-    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
-    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
-    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
-    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
+    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_restart(),
     "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
-    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
+    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
+    "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"),
 }))
 
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+    "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
+    "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
+    "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
+    "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
+}))
+
+
+options_templates.update(options_section(('img2img', "img2img"), {
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
+    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
+    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
+    "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
+    "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_restart(),
+    "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker,  {}).info("brush color of inpaint mask").needs_restart(),
+    "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_restart(),
+    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
+}))
+
+
 options_templates.update(options_section(('optimizations', "Optimizations"), {
     "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
-    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
     "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
     "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
     "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
@@ -448,7 +479,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), {
     "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
 }))
 
-options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+options_templates.update(options_section(('interrogate', "Interrogate"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
     "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
     "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
@@ -481,10 +512,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
 options_templates.update(options_section(('ui', "User interface"), {
     "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_restart(),
     "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(),
-    "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
-    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
     "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
@@ -503,11 +531,12 @@ options_templates.update(options_section(('ui', "User interface"), {
     "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
     "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(),
     "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_restart(),
-    "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires sampler selection").needs_restart(),
+    "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(),
     "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(),
     "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(),
 }))
 
+
 options_templates.update(options_section(('infotext', "Infotext"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
@@ -880,3 +909,10 @@ def walk_files(path, allowed_extensions=None):
                 continue
 
             yield os.path.join(root, filename)
+
+
+def ldm_print(*args, **kwargs):
+    if opts.hide_ldm_prints:
+        return
+
+    print(*args, **kwargs)

modules/styles.py

@@ -106,10 +106,7 @@ class StyleDatabase:
         if os.path.exists(path):
             shutil.copy(path, f"{path}.bak")
 
-        fd = os.open(path, os.O_RDWR | os.O_CREAT)
-        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
-            # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
-            # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
+        with open(path, "w", encoding="utf-8-sig", newline='') as file:
             writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
             writer.writeheader()
             writer.writerows(style._asdict() for k, style in self.styles.items())

modules/sysinfo.py

@@ -109,11 +109,15 @@ def format_traceback(tb):
     return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
 
 
+def format_exception(e, tb):
+    return {"exception": str(e), "traceback": format_traceback(tb)}
+
+
 def get_exceptions():
     try:
         from modules import errors
 
-        return [{"exception": str(e), "traceback": format_traceback(tb)} for e, tb in reversed(errors.exception_records)]
+        return list(reversed(errors.exception_records))
     except Exception as e:
         return str(e)
 

modules/textual_inversion/textual_inversion.py

@@ -13,7 +13,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from torch.utils.tensorboard import SummaryWriter
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -181,29 +181,38 @@ class EmbeddingDatabase:
         else:
             return
 
+
         # textual inversion embeddings
         if 'string_to_param' in data:
             param_dict = data['string_to_param']
             param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
             assert len(param_dict) == 1, 'embedding file has multiple terms in it'
             emb = next(iter(param_dict.items()))[1]
-        # diffuser concepts
-        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
+        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
+            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
+            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
+            vectors = data['clip_g'].shape[0]
+        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
             assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
 
             emb = next(iter(data.values()))
             if len(emb.shape) == 1:
                 emb = emb.unsqueeze(0)
+            vec = emb.detach().to(devices.device, dtype=torch.float32)
+            shape = vec.shape[-1]
+            vectors = vec.shape[0]
         else:
             raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
 
-        vec = emb.detach().to(devices.device, dtype=torch.float32)
         embedding = Embedding(vec, name)
         embedding.step = data.get('step', None)
         embedding.sd_checkpoint = data.get('sd_checkpoint', None)
         embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-        embedding.vectors = vec.shape[0]
-        embedding.shape = vec.shape[-1]
+        embedding.vectors = vectors
+        embedding.shape = shape
         embedding.filename = path
         embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
 
@@ -378,6 +387,8 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
 
 
 def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+    from modules import processing
+
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     template_file = textual_inversion_templates.get(template_filename, None)

modules/timer.py

@@ -1,4 +1,5 @@
 import time
+import argparse
 
 
 class TimerSubcategory:
@@ -11,20 +12,27 @@ class TimerSubcategory:
     def __enter__(self):
         self.start = time.time()
         self.timer.base_category = self.original_base_category + self.category + "/"
+        self.timer.subcategory_level += 1
+
+        if self.timer.print_log:
+            print(f"{'  ' * self.timer.subcategory_level}{self.category}:")
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         elapsed_for_subcategroy = time.time() - self.start
         self.timer.base_category = self.original_base_category
         self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
-        self.timer.record(self.category)
+        self.timer.subcategory_level -= 1
+        self.timer.record(self.category, disable_log=True)
 
 
 class Timer:
-    def __init__(self):
+    def __init__(self, print_log=False):
         self.start = time.time()
         self.records = {}
         self.total = 0
         self.base_category = ''
+        self.print_log = print_log
+        self.subcategory_level = 0
 
     def elapsed(self):
         end = time.time()
@@ -38,13 +46,16 @@ class Timer:
 
         self.records[category] += amount
 
-    def record(self, category, extra_time=0):
+    def record(self, category, extra_time=0, disable_log=False):
         e = self.elapsed()
 
         self.add_time_to_record(self.base_category + category, e + extra_time)
 
         self.total += e + extra_time
 
+        if self.print_log and not disable_log:
+            print(f"{'  ' * self.subcategory_level}{category}: done in {e + extra_time:.3f}s")
+
     def subcategory(self, name):
         self.elapsed()
 
@@ -71,6 +82,10 @@ class Timer:
         self.__init__()
 
 
-startup_timer = Timer()
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--log-startup", action='store_true', help="print a detailed log of what's happening at startup")
+args = parser.parse_known_args()[0]
+
+startup_timer = Timer(print_log=args.log_startup)
 
 startup_record = None

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_index: int, restore_faces: bool, tiling: bool, 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_sampler_index: int, 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_index: int, restore_faces: bool, tiling: bool, 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_index: int, 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(
@@ -41,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         hr_second_pass_steps=hr_second_pass_steps,
         hr_resize_x=hr_resize_x,
         hr_resize_y=hr_resize_y,
+        hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
         hr_sampler_name=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else None,
         hr_prompt=hr_prompt,
         hr_negative_prompt=hr_negative_prompt,

modules/ui.py

@@ -12,34 +12,30 @@ import numpy as np
 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 sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo
+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
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
 from modules.ui_gradio_extensions import reload_javascript
 
-
 from modules.shared import opts, cmd_opts
 
-import modules.codeformer_model
 import modules.generation_parameters_copypaste as parameters_copypaste
-import modules.gfpgan_model
-import modules.hypernetworks.ui
-import modules.scripts
+import modules.hypernetworks.ui as hypernetworks_ui
+import modules.textual_inversion.ui as textual_inversion_ui
+import modules.textual_inversion.textual_inversion as textual_inversion
 import modules.shared as shared
-import modules.styles
-import modules.textual_inversion.ui
+import modules.images
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
-from modules.textual_inversion import textual_inversion
-import modules.hypernetworks.ui
 from modules.generation_parameters_copypaste import image_from_url_text
-import modules.extras
 
 create_setting_component = ui_settings.create_setting_component
 
 warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -92,19 +88,6 @@ def send_gradio_gallery_to_image(x):
     return image_from_url_text(x[0])
 
 
-def add_style(name: str, prompt: str, negative_prompt: str):
-    if name is None:
-        return [gr_show() for x in range(4)]
-
-    style = modules.styles.PromptStyle(name, prompt, negative_prompt)
-    shared.prompt_styles.styles[style.name] = style
-    # Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
-    # reserialize all styles every time we save them
-    shared.prompt_styles.save_styles(shared.styles_filename)
-
-    return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
-
-
 def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
     from modules import processing, devices
 
@@ -129,13 +112,6 @@ def resize_from_to_html(width, height, scale_by):
     return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
 
 
-def apply_styles(prompt, prompt_neg, styles):
-    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
-    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
-
-    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
-
-
 def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
     if mode in {0, 1, 3, 4}:
         return [interrogation_function(ii_singles[mode]), None]
@@ -172,7 +148,6 @@ def interrogate_deepbooru(image):
 def create_seed_inputs(target_interface):
     with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
         seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
-        seed.style(container=False)
         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')
 
@@ -184,7 +159,6 @@ def create_seed_inputs(target_interface):
     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")
-        subseed.style(container=False)
         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")
@@ -267,70 +241,77 @@ def update_token_counter(text, steps):
     return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
 
 
-def create_toprow(is_img2img):
-    id_part = "img2img" if is_img2img else "txt2img"
+class Toprow:
+    """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
 
-    with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
-        with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+    def __init__(self, is_img2img):
+        id_part = "img2img" if is_img2img else "txt2img"
+        self.id_part = id_part
 
-            with gr.Row():
-                with gr.Column(scale=80):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+        with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+            with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+                with gr.Row():
+                    with gr.Column(scale=80):
+                        with gr.Row():
+                            self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+                            self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
 
-        button_interrogate = None
-        button_deepbooru = None
-        if is_img2img:
-            with gr.Column(scale=1, elem_classes="interrogate-col"):
-                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
-                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+                with gr.Row():
+                    with gr.Column(scale=80):
+                        with gr.Row():
+                            self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
 
-        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
-                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+            self.button_interrogate = None
+            self.button_deepbooru = None
+            if is_img2img:
+                with gr.Column(scale=1, elem_classes="interrogate-col"):
+                    self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                    self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
-                skip.click(
-                    fn=lambda: shared.state.skip(),
-                    inputs=[],
-                    outputs=[],
-                )
+            with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+                with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                    self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                    self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+                    self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
 
-                interrupt.click(
-                    fn=lambda: shared.state.interrupt(),
-                    inputs=[],
-                    outputs=[],
-                )
+                    self.skip.click(
+                        fn=lambda: shared.state.skip(),
+                        inputs=[],
+                        outputs=[],
+                    )
 
-            with gr.Row(elem_id=f"{id_part}_tools"):
-                paste = ToolButton(value=paste_symbol, elem_id="paste")
-                clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
-                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
-                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
-                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+                    self.interrupt.click(
+                        fn=lambda: shared.state.interrupt(),
+                        inputs=[],
+                        outputs=[],
+                    )
 
-                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
-                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
-                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+                with gr.Row(elem_id=f"{id_part}_tools"):
+                    self.paste = ToolButton(value=paste_symbol, elem_id="paste")
+                    self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+                    self.extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
+                    self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
 
-                clear_prompt_button.click(
-                    fn=lambda *x: x,
-                    _js="confirm_clear_prompt",
-                    inputs=[prompt, negative_prompt],
-                    outputs=[prompt, negative_prompt],
-                )
+                    self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+                    self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+                    self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+                    self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
 
-            with gr.Row(elem_id=f"{id_part}_styles_row"):
-                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
-                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
+                    self.clear_prompt_button.click(
+                        fn=lambda *x: x,
+                        _js="confirm_clear_prompt",
+                        inputs=[self.prompt, self.negative_prompt],
+                        outputs=[self.prompt, self.negative_prompt],
+                    )
 
-    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, None, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button
+                self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
+
+        self.prompt_img.change(
+            fn=modules.images.image_data,
+            inputs=[self.prompt_img],
+            outputs=[self.prompt, self.prompt_img],
+            show_progress=False,
+        )
 
 
 def setup_progressbar(*args, **kwargs):
@@ -414,21 +395,20 @@ def create_ui():
 
     parameters_copypaste.reset()
 
-    modules.scripts.scripts_current = modules.scripts.scripts_txt2img
-    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+    scripts.scripts_current = scripts.scripts_txt2img
+    scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, _, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)
+        toprow = Toprow(is_img2img=False)
 
         dummy_component = gr.Label(visible=False)
-        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
 
         extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
         extra_tabs.__enter__()
 
         with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, gr.Row().style(equal_height=False):
             with gr.Column(variant='compact', elem_id="txt2img_settings"):
-                modules.scripts.scripts_txt2img.prepare_ui()
+                scripts.scripts_txt2img.prepare_ui()
 
                 for category in ordered_ui_categories():
                     if category == "sampler":
@@ -474,6 +454,10 @@ def create_ui():
                                 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:
+
+                                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_index = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + [x.name for x in samplers_for_img2img], value="Use same sampler", type="index")
 
                             with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
@@ -496,10 +480,10 @@ def create_ui():
 
                     elif category == "scripts":
                         with FormGroup(elem_id="txt2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
+                            custom_inputs = scripts.scripts_txt2img.setup_ui()
 
                     else:
-                        modules.scripts.scripts_txt2img.setup_ui_for_section(category)
+                        scripts.scripts_txt2img.setup_ui_for_section(category)
 
             hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
 
@@ -530,9 +514,9 @@ def create_ui():
                 _js="submit",
                 inputs=[
                     dummy_component,
-                    txt2img_prompt,
-                    txt2img_negative_prompt,
-                    txt2img_prompt_styles,
+                    toprow.prompt,
+                    toprow.negative_prompt,
+                    toprow.ui_styles.dropdown,
                     steps,
                     sampler_index,
                     restore_faces,
@@ -551,6 +535,7 @@ def create_ui():
                     hr_second_pass_steps,
                     hr_resize_x,
                     hr_resize_y,
+                    hr_checkpoint_name,
                     hr_sampler_index,
                     hr_prompt,
                     hr_negative_prompt,
@@ -567,12 +552,12 @@ def create_ui():
                 show_progress=False,
             )
 
-            txt2img_prompt.submit(**txt2img_args)
-            submit.click(**txt2img_args)
+            toprow.prompt.submit(**txt2img_args)
+            toprow.submit.click(**txt2img_args)
 
             res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
 
-            restore_progress_button.click(
+            toprow.restore_progress_button.click(
                 fn=progress.restore_progress,
                 _js="restoreProgressTxt2img",
                 inputs=[dummy_component],
@@ -585,18 +570,6 @@ def create_ui():
                 show_progress=False,
             )
 
-            txt_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    txt_prompt_img
-                ],
-                outputs=[
-                    txt2img_prompt,
-                    txt_prompt_img
-                ],
-                show_progress=False,
-            )
-
             enable_hr.change(
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
@@ -605,8 +578,8 @@ def create_ui():
             )
 
             txt2img_paste_fields = [
-                (txt2img_prompt, "Prompt"),
-                (txt2img_negative_prompt, "Negative prompt"),
+                (toprow.prompt, "Prompt"),
+                (toprow.negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
@@ -615,34 +588,36 @@ def create_ui():
                 (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"),
-                (txt2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (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),
-                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
+                (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
+                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d))),
                 (hr_scale, "Hires upscale"),
                 (hr_upscaler, "Hires upscaler"),
                 (hr_second_pass_steps, "Hires steps"),
                 (hr_resize_x, "Hires resize-1"),
                 (hr_resize_y, "Hires resize-2"),
+                (hr_checkpoint_name, "Hires checkpoint"),
                 (hr_sampler_index, "Hires sampler"),
-                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" else gr.update()),
+                (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
                 (hr_prompt, "Hires prompt"),
                 (hr_negative_prompt, "Hires negative prompt"),
                 (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
-                *modules.scripts.scripts_txt2img.infotext_fields
+                *scripts.scripts_txt2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+                paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
             txt2img_preview_params = [
-                txt2img_prompt,
-                txt2img_negative_prompt,
+                toprow.prompt,
+                toprow.negative_prompt,
                 steps,
                 sampler_index,
                 cfg_scale,
@@ -651,8 +626,8 @@ def create_ui():
                 height,
             ]
 
-            token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+            toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+            toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
 
         from modules import ui_extra_networks
         extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
@@ -660,13 +635,11 @@ def create_ui():
 
         extra_tabs.__exit__()
 
-    modules.scripts.scripts_current = modules.scripts.scripts_img2img
-    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+    scripts.scripts_current = scripts.scripts_img2img
+    scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, _, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)
-
-        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
+        toprow = Toprow(is_img2img=True)
 
         extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
         extra_tabs.__enter__()
@@ -693,19 +666,19 @@ def create_ui():
                     img2img_selected_tab = gr.State(0)
 
                     with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
                         add_copy_image_controls('img2img', init_img)
 
                     with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
-                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
                         add_copy_image_controls('sketch', sketch)
 
                     with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
                         add_copy_image_controls('inpaint', init_img_with_mask)
 
                     with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
-                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=opts.img2img_editor_height)
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
                         inpaint_color_sketch_orig = gr.State(None)
                         add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
 
@@ -765,7 +738,7 @@ def create_ui():
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
-                modules.scripts.scripts_img2img.prepare_ui()
+                scripts.scripts_img2img.prepare_ui()
 
                 for category in ordered_ui_categories():
                     if category == "sampler":
@@ -846,7 +819,7 @@ def create_ui():
 
                     elif category == "scripts":
                         with FormGroup(elem_id="img2img_script_container"):
-                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
+                            custom_inputs = scripts.scripts_img2img.setup_ui()
 
                     elif category == "inpaint":
                         with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
@@ -877,34 +850,22 @@ def create_ui():
                                     outputs=[inpaint_controls, mask_alpha],
                                 )
                     else:
-                        modules.scripts.scripts_img2img.setup_ui_for_section(category)
+                        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_prompt_img.change(
-                fn=modules.images.image_data,
-                inputs=[
-                    img2img_prompt_img
-                ],
-                outputs=[
-                    img2img_prompt,
-                    img2img_prompt_img
-                ],
-                show_progress=False,
-            )
-
             img2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
                 inputs=[
                     dummy_component,
                     dummy_component,
-                    img2img_prompt,
-                    img2img_negative_prompt,
-                    img2img_prompt_styles,
+                    toprow.prompt,
+                    toprow.negative_prompt,
+                    toprow.ui_styles.dropdown,
                     init_img,
                     sketch,
                     init_img_with_mask,
@@ -963,11 +924,11 @@ def create_ui():
                     inpaint_color_sketch,
                     init_img_inpaint,
                 ],
-                outputs=[img2img_prompt, dummy_component],
+                outputs=[toprow.prompt, dummy_component],
             )
 
-            img2img_prompt.submit(**img2img_args)
-            submit.click(**img2img_args)
+            toprow.prompt.submit(**img2img_args)
+            toprow.submit.click(**img2img_args)
 
             res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
 
@@ -979,7 +940,7 @@ def create_ui():
                 show_progress=False,
             )
 
-            restore_progress_button.click(
+            toprow.restore_progress_button.click(
                 fn=progress.restore_progress,
                 _js="restoreProgressImg2img",
                 inputs=[dummy_component],
@@ -992,44 +953,22 @@ def create_ui():
                 show_progress=False,
             )
 
-            img2img_interrogate.click(
+            toprow.button_interrogate.click(
                 fn=lambda *args: process_interrogate(interrogate, *args),
                 **interrogate_args,
             )
 
-            img2img_deepbooru.click(
+            toprow.button_deepbooru.click(
                 fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
                 **interrogate_args,
             )
 
-            prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
-            style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
-            style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
-
-            for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
-                button.click(
-                    fn=add_style,
-                    _js="ask_for_style_name",
-                    # Have to pass empty dummy component here, because the JavaScript and Python function have to accept
-                    # the same number of parameters, but we only know the style-name after the JavaScript prompt
-                    inputs=[dummy_component, prompt, negative_prompt],
-                    outputs=[txt2img_prompt_styles, img2img_prompt_styles],
-                )
-
-            for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
-                button.click(
-                    fn=apply_styles,
-                    _js=js_func,
-                    inputs=[prompt, negative_prompt, styles],
-                    outputs=[prompt, negative_prompt, styles],
-                )
-
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
+            toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+            toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
 
             img2img_paste_fields = [
-                (img2img_prompt, "Prompt"),
-                (img2img_negative_prompt, "Negative prompt"),
+                (toprow.prompt, "Prompt"),
+                (toprow.negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
                 (sampler_index, "Sampler"),
                 (restore_faces, "Face restoration"),
@@ -1039,19 +978,20 @@ def create_ui():
                 (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"),
-                (img2img_prompt_styles, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+                (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.scripts.scripts_img2img.infotext_fields
+                *scripts.scripts_img2img.infotext_fields
             ]
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
             parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
-                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+                paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
             ))
 
         from modules import ui_extra_networks
@@ -1060,13 +1000,13 @@ def create_ui():
 
         extra_tabs.__exit__()
 
-    modules.scripts.scripts_current = None
+    scripts.scripts_current = None
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         ui_postprocessing.create_ui()
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
-        with gr.Row().style(equal_height=False):
+        with gr.Row(equal_height=False):
             with gr.Column(variant='panel'):
                 image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
 
@@ -1088,64 +1028,13 @@ def create_ui():
             outputs=[html, generation_info, html2],
         )
 
-    def update_interp_description(value):
-        interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
-        interp_descriptions = {
-            "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
-            "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
-            "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
-        }
-        return interp_descriptions[value]
-
-    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
-        with gr.Row().style(equal_height=False):
-            with gr.Column(variant='compact'):
-                interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
-
-                with FormRow(elem_id="modelmerger_models"):
-                    primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
-                    create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
-
-                    secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
-                    create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
-
-                    tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
-                    create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
-
-                custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
-                interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
-                interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
-                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
-
-                with FormRow():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
-                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
-                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
-
-                with FormRow():
-                    with gr.Column():
-                        config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
-
-                    with gr.Column():
-                        with FormRow():
-                            bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
-                            create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
-
-                with FormRow():
-                    discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
-
-                with gr.Row():
-                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
-
-            with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
-                with gr.Group(elem_id="modelmerger_results_panel"):
-                    modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+    modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
 
     with gr.Blocks(analytics_enabled=False) as train_interface:
-        with gr.Row().style(equal_height=False):
+        with gr.Row(equal_height=False):
             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-        with gr.Row(variant="compact").style(equal_height=False):
+        with gr.Row(variant="compact", equal_height=False):
             with gr.Tabs(elem_id="train_tabs"):
 
                 with gr.Tab(label="Create embedding", id="create_embedding"):
@@ -1165,7 +1054,7 @@ def create_ui():
                     new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
                     new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
@@ -1305,12 +1194,12 @@ def create_ui():
 
             with gr.Column(elem_id='ti_gallery_container'):
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
                 gr.HTML(elem_id="ti_progress", value="")
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
         create_embedding.click(
-            fn=modules.textual_inversion.ui.create_embedding,
+            fn=textual_inversion_ui.create_embedding,
             inputs=[
                 new_embedding_name,
                 initialization_text,
@@ -1325,7 +1214,7 @@ def create_ui():
         )
 
         create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
+            fn=hypernetworks_ui.create_hypernetwork,
             inputs=[
                 new_hypernetwork_name,
                 new_hypernetwork_sizes,
@@ -1345,7 +1234,7 @@ def create_ui():
         )
 
         run_preprocess.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
@@ -1381,7 +1270,7 @@ def create_ui():
         )
 
         train_embedding.click(
-            fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
@@ -1415,7 +1304,7 @@ def create_ui():
         )
 
         train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
+            fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
             inputs=[
                 dummy_component,
@@ -1469,7 +1358,7 @@ def create_ui():
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
+        (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "train"),
     ]
 
@@ -1521,49 +1410,11 @@ def create_ui():
         settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
         demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
 
-        def modelmerger(*args):
-            try:
-                results = modules.extras.run_modelmerger(*args)
-            except Exception as e:
-                errors.report("Error loading/saving model file", exc_info=True)
-                modules.sd_models.list_models()  # to remove the potentially missing models from the list
-                return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
-            return results
-
-        modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
-        modelmerger_merge.click(
-            fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
-            _js='modelmerger',
-            inputs=[
-                dummy_component,
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                interp_method,
-                interp_amount,
-                save_as_half,
-                custom_name,
-                checkpoint_format,
-                config_source,
-                bake_in_vae,
-                discard_weights,
-                save_metadata,
-            ],
-            outputs=[
-                primary_model_name,
-                secondary_model_name,
-                tertiary_model_name,
-                settings.component_dict['sd_model_checkpoint'],
-                modelmerger_result,
-            ]
-        )
+        modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
 
     loadsave.dump_defaults()
     demo.ui_loadsave = loadsave
 
-    # Required as a workaround for change() event not triggering when loading values from ui-config.json
-    interp_description.value = update_interp_description(interp_method.value)
-
     return demo
 
 

modules/ui_checkpoint_merger.py

@@ -0,0 +1,124 @@
+
+import gradio as gr
+
+from modules import sd_models, sd_vae, errors, extras, call_queue
+from modules.ui_components import FormRow
+from modules.ui_common import create_refresh_button
+
+
+def update_interp_description(value):
+    interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+    interp_descriptions = {
+        "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+        "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+        "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+    }
+    return interp_descriptions[value]
+
+
+def modelmerger(*args):
+    try:
+        results = extras.run_modelmerger(*args)
+    except Exception as e:
+        errors.report("Error loading/saving model file", exc_info=True)
+        sd_models.list_models()  # to remove the potentially missing models from the list
+        return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+    return results
+
+
+class UiCheckpointMerger:
+    def __init__(self):
+        with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+            with gr.Row(equal_height=False):
+                with gr.Column(variant='compact'):
+                    self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+                    with FormRow(elem_id="modelmerger_models"):
+                        self.primary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+                        create_refresh_button(self.primary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+                        self.secondary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+                        create_refresh_button(self.secondary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+                        self.tertiary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+                        create_refresh_button(self.tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+                    self.custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+                    self.interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+                    self.interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+                    self.interp_method.change(fn=update_interp_description, inputs=[self.interp_method], outputs=[self.interp_description])
+
+                    with FormRow():
+                        self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                        self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+
+                    with FormRow():
+                        with gr.Column():
+                            self.config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+                        with gr.Column():
+                            with FormRow():
+                                self.bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+                                create_refresh_button(self.bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+                    with FormRow():
+                        self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+                    with gr.Accordion("Metadata", open=False) as metadata_editor:
+                        with FormRow():
+                            self.save_metadata = gr.Checkbox(value=True, label="Save metadata", elem_id="modelmerger_save_metadata")
+                            self.add_merge_recipe = gr.Checkbox(value=True, label="Add merge recipe metadata", elem_id="modelmerger_add_recipe")
+                            self.copy_metadata_fields = gr.Checkbox(value=True, label="Copy metadata from merged models", elem_id="modelmerger_copy_metadata")
+
+                        self.metadata_json = gr.TextArea('{}', label="Metadata in JSON format")
+                        self.read_metadata = gr.Button("Read metadata from selected checkpoints")
+
+                    with FormRow():
+                        self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+                with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+                    with gr.Group(elem_id="modelmerger_results_panel"):
+                        self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+        self.metadata_editor = metadata_editor
+        self.blocks = modelmerger_interface
+
+    def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+        self.checkpoint_format.change(lambda fmt: gr.update(visible=fmt == 'safetensors'), inputs=[self.checkpoint_format], outputs=[self.metadata_editor], show_progress=False)
+
+        self.read_metadata.click(extras.read_metadata, inputs=[self.primary_model_name, self.secondary_model_name, self.tertiary_model_name], outputs=[self.metadata_json])
+
+        self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
+        self.modelmerger_merge.click(
+            fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+            _js='modelmerger',
+            inputs=[
+                dummy_component,
+                self.primary_model_name,
+                self.secondary_model_name,
+                self.tertiary_model_name,
+                self.interp_method,
+                self.interp_amount,
+                self.save_as_half,
+                self.custom_name,
+                self.checkpoint_format,
+                self.config_source,
+                self.bake_in_vae,
+                self.discard_weights,
+                self.save_metadata,
+                self.add_merge_recipe,
+                self.copy_metadata_fields,
+                self.metadata_json,
+            ],
+            outputs=[
+                self.primary_model_name,
+                self.secondary_model_name,
+                self.tertiary_model_name,
+                sd_model_checkpoint_component,
+                self.modelmerger_result,
+            ]
+        )
+
+        # Required as a workaround for change() event not triggering when loading values from ui-config.json
+        self.interp_description.value = update_interp_description(self.interp_method.value)
+

modules/ui_common.py

@@ -134,7 +134,7 @@ Requested path was: {f}
 
     with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
         with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4)
 
         generation_info = None
         with gr.Column():
@@ -223,20 +223,44 @@ Requested path was: {f}
 
 
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+    refresh_components = refresh_component if isinstance(refresh_component, list) else [refresh_component]
+
+    label = None
+    for comp in refresh_components:
+        label = getattr(comp, 'label', None)
+        if label is not None:
+            break
+
     def refresh():
         refresh_method()
         args = refreshed_args() if callable(refreshed_args) else refreshed_args
 
         for k, v in args.items():
-            setattr(refresh_component, k, v)
+            for comp in refresh_components:
+                setattr(comp, k, v)
 
-        return gr.update(**(args or {}))
+        return (gr.update(**(args or {})) for _ in refresh_components) if len(refresh_components) > 1 else gr.update(**(args or {}))
 
-    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id)
+    refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id, tooltip=f"{label}: refresh" if label else "Refresh")
     refresh_button.click(
         fn=refresh,
         inputs=[],
-        outputs=[refresh_component]
+        outputs=refresh_components
     )
     return refresh_button
 
+
+def setup_dialog(button_show, dialog, *, button_close=None):
+    """Sets up the UI so that the dialog (gr.Box) is invisible, and is only shown when buttons_show is clicked, in a fullscreen modal window."""
+
+    dialog.visible = False
+
+    button_show.click(
+        fn=lambda: gr.update(visible=True),
+        inputs=[],
+        outputs=[dialog],
+    ).then(fn=None, _js="function(){ popup(gradioApp().getElementById('" + dialog.elem_id + "')); }")
+
+    if button_close:
+        button_close.click(fn=None, _js="closePopup")
+

modules/ui_components.py

@@ -35,7 +35,7 @@ class FormColumn(FormComponent, gr.Column):
 
 
 class FormGroup(FormComponent, gr.Group):
-    """Same as gr.Row but fits inside gradio forms"""
+    """Same as gr.Group but fits inside gradio forms"""
 
     def get_block_name(self):
         return "group"

modules/ui_extensions.py

@@ -164,7 +164,7 @@ def extension_table():
             ext_status = ext.status
 
         style = ""
-        if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+        if shared.cmd_opts.disable_extra_extensions and not ext.is_builtin or shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
             style = STYLE_PRIMARY
 
         version_link = ext.version
@@ -533,16 +533,20 @@ def create_ui():
                     apply = gr.Button(value=apply_label, variant="primary")
                     check = gr.Button(value="Check for updates")
                     extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
-                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
-                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
+                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
+                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
 
                 html = ""
-                if shared.opts.disable_all_extensions != "none":
-                    html = """
-<span style="color: var(--primary-400);">
-    "Disable all extensions" was set, change it to "none" to load all extensions again
-</span>
-                    """
+
+                if shared.cmd_opts.disable_all_extensions or shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions != "none":
+                    if shared.cmd_opts.disable_all_extensions:
+                        msg = '"--disable-all-extensions" was used, remove it to load all extensions again'
+                    elif shared.opts.disable_all_extensions != "none":
+                        msg = '"Disable all extensions" was set, change it to "none" to load all extensions again'
+                    elif shared.cmd_opts.disable_extra_extensions:
+                        msg = '"--disable-extra-extensions" was used, remove it to load all extensions again'
+                    html = f'<span style="color: var(--primary-400);">{msg}</span>'
+
                 info = gr.HTML(html)
                 extensions_table = gr.HTML('Loading...')
                 ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
@@ -565,7 +569,7 @@ def create_ui():
                 with gr.Row():
                     refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
                     extensions_index_url = os.environ.get('WEBUI_EXTENSIONS_INDEX', "https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json")
-                    available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL").style(container=False)
+                    available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL", container=False)
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
@@ -574,7 +578,7 @@ def create_ui():
                     sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
 
                 with gr.Row():
-                    search_extensions_text = gr.Text(label="Search").style(container=False)
+                    search_extensions_text = gr.Text(label="Search", container=False)
 
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()

modules/ui_extra_networks.py

@@ -2,7 +2,7 @@ import os.path
 import urllib.parse
 from pathlib import Path
 
-from modules import shared, ui_extra_networks_user_metadata, errors
+from modules import shared, ui_extra_networks_user_metadata, errors, extra_networks
 from modules.images import read_info_from_image, save_image_with_geninfo
 from modules.ui import up_down_symbol
 import gradio as gr
@@ -62,7 +62,8 @@ def get_single_card(page: str = "", tabname: str = "", name: str = ""):
     page = next(iter([x for x in extra_pages if x.name == page]), None)
 
     try:
-        item = page.create_item(name)
+        item = page.create_item(name, enable_filter=False)
+        page.items[name] = item
     except Exception as e:
         errors.display(e, "creating item for extra network")
         item = page.items.get(name)
@@ -100,16 +101,7 @@ class ExtraNetworksPage:
 
     def read_user_metadata(self, item):
         filename = item.get("filename", None)
-        basename, ext = os.path.splitext(filename)
-        metadata_filename = basename + '.json'
-
-        metadata = {}
-        try:
-            if os.path.isfile(metadata_filename):
-                with open(metadata_filename, "r", encoding="utf8") as file:
-                    metadata = json.load(file)
-        except Exception as e:
-            errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+        metadata = extra_networks.get_user_metadata(filename)
 
         desc = metadata.get("description", None)
         if desc is not None:
@@ -252,7 +244,7 @@ class ExtraNetworksPage:
             "prompt": item.get("prompt", None),
             "tabname": quote_js(tabname),
             "local_preview": quote_js(item["local_preview"]),
-            "name": item["name"],
+            "name": html.escape(item["name"]),
             "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
             "card_clicked": onclick,
             "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',

modules/ui_extra_networks_checkpoints.py

@@ -3,6 +3,7 @@ import os
 
 from modules import shared, ui_extra_networks, sd_models
 from modules.ui_extra_networks import quote_js
+from modules.ui_extra_networks_checkpoints_user_metadata import CheckpointUserMetadataEditor
 
 
 class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
@@ -12,7 +13,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         shared.refresh_checkpoints()
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
         path, ext = os.path.splitext(checkpoint.filename)
         return {
@@ -23,6 +24,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
             "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
             "onclick": '"' + html.escape(f"""return selectCheckpoint({quote_js(name)})""") + '"',
             "local_preview": f"{path}.{shared.opts.samples_format}",
+            "metadata": checkpoint.metadata,
             "sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
         }
 
@@ -33,3 +35,5 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
     def allowed_directories_for_previews(self):
         return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
 
+    def create_user_metadata_editor(self, ui, tabname):
+        return CheckpointUserMetadataEditor(ui, tabname, self)

modules/ui_extra_networks_checkpoints_user_metadata.py

@@ -0,0 +1,60 @@
+import gradio as gr
+
+from modules import ui_extra_networks_user_metadata, sd_vae
+from modules.ui_common import create_refresh_button
+
+
+class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
+    def __init__(self, ui, tabname, page):
+        super().__init__(ui, tabname, page)
+
+        self.select_vae = None
+
+    def save_user_metadata(self, name, desc, notes, vae):
+        user_metadata = self.get_user_metadata(name)
+        user_metadata["description"] = desc
+        user_metadata["notes"] = notes
+        user_metadata["vae"] = vae
+
+        self.write_user_metadata(name, user_metadata)
+
+    def put_values_into_components(self, name):
+        user_metadata = self.get_user_metadata(name)
+        values = super().put_values_into_components(name)
+
+        return [
+            *values[0:5],
+            user_metadata.get('vae', ''),
+        ]
+
+    def create_editor(self):
+        self.create_default_editor_elems()
+
+        with gr.Row():
+            self.select_vae = gr.Dropdown(choices=["Automatic", "None"] + list(sd_vae.vae_dict), value="None", label="Preferred VAE", elem_id="checpoint_edit_user_metadata_preferred_vae")
+            create_refresh_button(self.select_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, "checpoint_edit_user_metadata_refresh_preferred_vae")
+
+        self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+        self.create_default_buttons()
+
+        viewed_components = [
+            self.edit_name,
+            self.edit_description,
+            self.html_filedata,
+            self.html_preview,
+            self.edit_notes,
+            self.select_vae,
+        ]
+
+        self.button_edit\
+            .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
+            .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+        edited_components = [
+            self.edit_description,
+            self.edit_notes,
+            self.select_vae,
+        ]
+
+        self.setup_save_handler(self.button_save, self.save_user_metadata, edited_components)

modules/ui_extra_networks_hypernets.py

@@ -11,7 +11,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         shared.reload_hypernetworks()
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         full_path = shared.hypernetworks[name]
         path, ext = os.path.splitext(full_path)
 

modules/ui_extra_networks_textual_inversion.py

@@ -12,7 +12,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
     def refresh(self):
         sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
 
-    def create_item(self, name, index=None):
+    def create_item(self, name, index=None, enable_filter=True):
         embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
 
         path, ext = os.path.splitext(embedding.filename)

modules/ui_extra_networks_user_metadata.py

@@ -42,6 +42,9 @@ class UserMetadataEditor:
 
         return user_metadata
 
+    def create_extra_default_items_in_left_column(self):
+        pass
+
     def create_default_editor_elems(self):
         with gr.Row():
             with gr.Column(scale=2):
@@ -49,6 +52,8 @@ class UserMetadataEditor:
                 self.edit_description = gr.Textbox(label="Description", lines=4)
                 self.html_filedata = gr.HTML()
 
+                self.create_extra_default_items_in_left_column()
+
             with gr.Column(scale=1, min_width=0):
                 self.html_preview = gr.HTML()
 
@@ -91,6 +96,7 @@ class UserMetadataEditor:
 
             stats = os.stat(filename)
             params = [
+                ('Filename: ', os.path.basename(filename)),
                 ('File size: ', sysinfo.pretty_bytes(stats.st_size)),
                 ('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
             ]
@@ -111,7 +117,7 @@ class UserMetadataEditor:
 
         table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params) + '</table>'
 
-        return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', ''),
+        return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
 
     def write_user_metadata(self, name, metadata):
         item = self.page.items.get(name, {})

modules/ui_postprocessing.py

@@ -6,7 +6,7 @@ import modules.generation_parameters_copypaste as parameters_copypaste
 def create_ui():
     tab_index = gr.State(value=0)
 
-    with gr.Row().style(equal_height=False, variant='compact'):
+    with gr.Row(equal_height=False, variant='compact'):
         with gr.Column(variant='compact'):
             with gr.Tabs(elem_id="mode_extras"):
                 with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:

modules/ui_prompt_styles.py

@@ -0,0 +1,110 @@
+import gradio as gr
+
+from modules import shared, ui_common, ui_components, styles
+
+styles_edit_symbol = '\U0001f58c\uFE0F'  # 🖌️
+styles_materialize_symbol = '\U0001f4cb'  # 📋
+
+
+def select_style(name):
+    style = shared.prompt_styles.styles.get(name)
+    existing = style is not None
+    empty = not name
+
+    prompt = style.prompt if style else gr.update()
+    negative_prompt = style.negative_prompt if style else gr.update()
+
+    return prompt, negative_prompt, gr.update(visible=existing), gr.update(visible=not empty)
+
+
+def save_style(name, prompt, negative_prompt):
+    if not name:
+        return gr.update(visible=False)
+
+    style = styles.PromptStyle(name, prompt, negative_prompt)
+    shared.prompt_styles.styles[style.name] = style
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return gr.update(visible=True)
+
+
+def delete_style(name):
+    if name == "":
+        return
+
+    shared.prompt_styles.styles.pop(name, None)
+    shared.prompt_styles.save_styles(shared.styles_filename)
+
+    return '', '', ''
+
+
+def materialize_styles(prompt, negative_prompt, styles):
+    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+    negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(negative_prompt, styles)
+
+    return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=negative_prompt), gr.Dropdown.update(value=[])]
+
+
+def refresh_styles():
+    return gr.update(choices=list(shared.prompt_styles.styles)), gr.update(choices=list(shared.prompt_styles.styles))
+
+
+class UiPromptStyles:
+    def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
+        self.tabname = tabname
+
+        with gr.Row(elem_id=f"{tabname}_styles_row"):
+            self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
+            edit_button = ui_components.ToolButton(value=styles_edit_symbol, elem_id=f"{tabname}_styles_edit_button", tooltip="Edit styles")
+
+        with gr.Box(elem_id=f"{tabname}_styles_dialog", elem_classes="popup-dialog") as styles_dialog:
+            with gr.Row():
+                self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
+                ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
+                self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+
+            with gr.Row():
+                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
+
+            with gr.Row():
+                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
+
+            with gr.Row():
+                self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
+                self.delete = gr.Button('Delete', variant='primary', elem_id=f'{tabname}_edit_style_delete', visible=False)
+                self.close = gr.Button('Close', variant='secondary', elem_id=f'{tabname}_edit_style_close')
+
+        self.selection.change(
+            fn=select_style,
+            inputs=[self.selection],
+            outputs=[self.prompt, self.neg_prompt, self.delete, self.save],
+            show_progress=False,
+        )
+
+        self.save.click(
+            fn=save_style,
+            inputs=[self.selection, self.prompt, self.neg_prompt],
+            outputs=[self.delete],
+            show_progress=False,
+        ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+        self.delete.click(
+            fn=delete_style,
+            _js='function(name){ if(name == "") return ""; return confirm("Delete style " + name + "?") ? name : ""; }',
+            inputs=[self.selection],
+            outputs=[self.selection, self.prompt, self.neg_prompt],
+            show_progress=False,
+        ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+        self.materialize.click(
+            fn=materialize_styles,
+            inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+            outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+            show_progress=False,
+        ).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
+
+        ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
+
+
+
+

modules/ui_settings.py

@@ -158,7 +158,7 @@ class UiSettings:
                     loadsave.create_ui()
 
                 with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
-                    gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo">(or open as text in a new page)</a>', elem_id="sysinfo_download")
+                    gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo" target="_blank">(or open as text in a new page)</a>', elem_id="sysinfo_download")
 
                     with gr.Row():
                         with gr.Column(scale=1):

requirements.txt

@@ -7,13 +7,14 @@ blendmodes
 clean-fid
 einops
 gfpgan
-gradio==3.32.0
+gradio==3.39.0
 inflection
 jsonmerge
 kornia
 lark
 numpy
 omegaconf
+open-clip-torch
 
 piexif
 psutil
@@ -29,4 +30,4 @@ tomesd
 torch
 torchdiffeq
 torchsde
-transformers==4.25.1
+transformers==4.30.2

requirements_versions.txt

@@ -1,30 +1,31 @@
-GitPython==3.1.30
+GitPython==3.1.32
 Pillow==9.5.0
-accelerate==0.18.0
+accelerate==0.21.0
 basicsr==1.4.2
 blendmodes==2022
 clean-fid==0.1.35
 einops==0.4.1
 fastapi==0.94.0
 gfpgan==1.3.8
-gradio==3.32.0
-httpcore<=0.15
+gradio==3.39.0
+httpcore==0.15
 inflection==0.5.1
 jsonmerge==1.8.0
 kornia==0.6.7
 lark==1.1.2
 numpy==1.23.5
 omegaconf==2.2.3
+open-clip-torch==2.20.0
 piexif==1.1.3
-psutil~=5.9.5
+psutil==5.9.5
 pytorch_lightning==1.9.4
 realesrgan==0.3.0
 resize-right==0.0.2
 safetensors==0.3.1
-scikit-image==0.20.0
-timm==0.6.7
-tomesd==0.1.2
+scikit-image==0.21.0
+timm==0.9.2
+tomesd==0.1.3
 torch
 torchdiffeq==0.2.3
 torchsde==0.2.5
-transformers==4.25.1
+transformers==4.30.2

scripts/xyz_grid.py

@@ -3,6 +3,7 @@ from copy import copy
 from itertools import permutations, chain
 import random
 import csv
+import os.path
 from io import StringIO
 from PIL import Image
 import numpy as np
@@ -10,7 +11,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion
+from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, errors
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, state
 import modules.shared as shared
@@ -66,14 +67,6 @@ def apply_order(p, x, xs):
     p.prompt = prompt_tmp + p.prompt
 
 
-def apply_sampler(p, x, xs):
-    sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
-    if sampler_name is None:
-        raise RuntimeError(f"Unknown sampler: {x}")
-
-    p.sampler_name = sampler_name
-
-
 def confirm_samplers(p, xs):
     for x in xs:
         if x.lower() not in sd_samplers.samplers_map:
@@ -144,11 +137,20 @@ def apply_face_restore(p, opt, x):
     p.restore_faces = is_active
 
 
-def apply_override(field):
+def apply_override(field, boolean: bool = False):
     def fun(p, x, xs):
+        if boolean:
+            x = True if x.lower() == "true" else False
         p.override_settings[field] = x
     return fun
 
+
+def boolean_choice(reverse: bool = False):
+    def choice():
+        return ["False", "True"] if reverse else ["True", "False"]
+    return choice
+
+
 def format_value_add_label(p, opt, x):
     if type(x) == float:
         x = round(x, 8)
@@ -173,6 +175,8 @@ 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"""
@@ -212,9 +216,10 @@ axis_options = [
     AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
     AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
     AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
-    AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
-    AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
+    AxisOptionTxt2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
+    AxisOptionTxt2Img("Hires sampler", str, apply_field("hr_sampler_name"), confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
+    AxisOptionImg2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_remove_path, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
     AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
     AxisOption("Sigma Churn", float, apply_field("s_churn")),
     AxisOption("Sigma min", float, apply_field("s_tmin")),
@@ -235,6 +240,7 @@ axis_options = [
     AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
     AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
     AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
+    AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)),
 ]
 
 
@@ -638,7 +644,12 @@ class Script(scripts.Script):
             y_opt.apply(pc, y, ys)
             z_opt.apply(pc, z, zs)
 
-            res = process_images(pc)
+            try:
+                res = process_images(pc)
+            except Exception as e:
+                errors.display(e, "generating image for xyz plot")
+
+                res = Processed(p, [], p.seed, "")
 
             # Sets subgrid infotexts
             subgrid_index = 1 + iz

style.css

@@ -8,6 +8,7 @@
     --checkbox-label-gap: 0.25em 0.1em;
     --section-header-text-size: 12pt;
     --block-background-fill: transparent;
+
 }
 
 .block.padded:not(.gradio-accordion) {
@@ -42,7 +43,8 @@ div.form{
 .block.gradio-radio,
 .block.gradio-checkboxgroup,
 .block.gradio-number,
-.block.gradio-colorpicker
+.block.gradio-colorpicker,
+div.gradio-group
 {
     border-width: 0 !important;
     box-shadow: none !important;
@@ -133,6 +135,15 @@ a{
     cursor: pointer;
 }
 
+div.styler{
+    border: none;
+    background: var(--background-fill-primary);
+}
+
+.block.gradio-textbox{
+    overflow: visible !important;
+}
+
 
 /* general styled components */
 
@@ -164,7 +175,7 @@ a{
 .checkboxes-row > div{
     flex: 0;
     white-space: nowrap;
-    min-width: auto;
+    min-width: auto !important;
 }
 
 button.custom-button{
@@ -388,6 +399,7 @@ div#extras_scale_to_tab div.form{
 #quicksettings > div, #quicksettings > fieldset{
     max-width: 24em;
     min-width: 24em;
+    width: 24em;
     padding: 0;
     border: none;
     box-shadow: none;
@@ -423,15 +435,16 @@ div#extras_scale_to_tab div.form{
 }
 
 table.popup-table{
-    background: white;
+    background: var(--body-background-fill);
+    color: var(--body-text-color);
     border-collapse: collapse;
     margin: 1em;
-    border: 4px solid white;
+    border: 4px solid var(--body-background-fill);
 }
 
 table.popup-table td{
     padding: 0.4em;
-    border: 1px solid #ccc;
+    border: 1px solid rgba(128, 128, 128, 0.5);
     max-width: 36em;
 }
 
@@ -845,7 +858,7 @@ footer {
 
 .extra-network-cards .card .card-button {
     text-shadow: 2px 2px 3px black;
-    padding: 0.25em;
+    padding: 0.25em 0.1em;
     font-size: 200%;
     width: 1.5em;
 }
@@ -961,6 +974,10 @@ div.block.gradio-box.edit-user-metadata {
     text-align: left;
 }
 
+.edit-user-metadata .file-metadata th, .edit-user-metadata .file-metadata td{
+    padding: 0.3em 1em;
+}
+
 .edit-user-metadata .wrap.translucent{
     background: var(--body-background-fill);
 }
@@ -971,3 +988,16 @@ div.block.gradio-box.edit-user-metadata {
 .edit-user-metadata-buttons{
     margin-top: 1.5em;
 }
+
+
+
+
+div.block.gradio-box.popup-dialog, .popup-dialog {
+    width: 56em;
+    background: var(--body-background-fill);
+    padding: 2em !important;
+}
+
+div.block.gradio-box.popup-dialog > div:last-child, .popup-dialog > div:last-child{
+    margin-top: 1em;
+}

webui.py

@@ -14,7 +14,6 @@ from typing import Iterable
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
-from packaging import version
 
 import logging
 
@@ -31,24 +30,26 @@ if log_level:
 logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
 logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
-from modules import paths, timer, import_hook, errors, devices  # noqa: F401
-
+from modules import timer
 startup_timer = timer.startup_timer
+startup_timer.record("launcher")
 
 import torch
 import pytorch_lightning   # noqa: F401 # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them
 warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
 warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
-
-
 startup_timer.record("import torch")
 
 import gradio  # noqa: F401
 startup_timer.record("import gradio")
 
+from modules import paths, timer, import_hook, errors, devices  # noqa: F401
+startup_timer.record("setup paths")
+
 import ldm.modules.encoders.modules  # noqa: F401
 startup_timer.record("import ldm")
 
+
 from modules import extra_networks
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, queue_lock  # noqa: F401
 
@@ -57,10 +58,15 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
     torch.__long_version__ = torch.__version__
     torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
 
-from modules import shared, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states
+from modules import shared
+
+if not shared.cmd_opts.skip_version_check:
+    errors.check_versions()
+
 import modules.codeformer_model as codeformer
-import modules.face_restoration
 import modules.gfpgan_model as gfpgan
+from modules import sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states
+import modules.face_restoration
 import modules.img2img
 
 import modules.lowvram
@@ -129,37 +135,6 @@ def fix_asyncio_event_loop_policy():
     asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
 
 
-def check_versions():
-    if shared.cmd_opts.skip_version_check:
-        return
-
-    expected_torch_version = "2.0.0"
-
-    if version.parse(torch.__version__) < version.parse(expected_torch_version):
-        errors.print_error_explanation(f"""
-You are running torch {torch.__version__}.
-The program is tested to work with torch {expected_torch_version}.
-To reinstall the desired version, run with commandline flag --reinstall-torch.
-Beware that this will cause a lot of large files to be downloaded, as well as
-there are reports of issues with training tab on the latest version.
-
-Use --skip-version-check commandline argument to disable this check.
-        """.strip())
-
-    expected_xformers_version = "0.0.20"
-    if shared.xformers_available:
-        import xformers
-
-        if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
-            errors.print_error_explanation(f"""
-You are running xformers {xformers.__version__}.
-The program is tested to work with xformers {expected_xformers_version}.
-To reinstall the desired version, run with commandline flag --reinstall-xformers.
-
-Use --skip-version-check commandline argument to disable this check.
-            """.strip())
-
-
 def restore_config_state_file():
     config_state_file = shared.opts.restore_config_state_file
     if config_state_file == "":
@@ -247,7 +222,6 @@ def initialize():
     fix_asyncio_event_loop_policy()
     validate_tls_options()
     configure_sigint_handler()
-    check_versions()
     modelloader.cleanup_models()
     configure_opts_onchange()
 
@@ -319,9 +293,9 @@ def initialize_rest(*, reload_script_modules=False):
         if modules.sd_hijack.current_optimizer is None:
             modules.sd_hijack.apply_optimizations()
 
-    Thread(target=load_model).start()
+        devices.first_time_calculation()
 
-    Thread(target=devices.first_time_calculation).start()
+    Thread(target=load_model).start()
 
     shared.reload_hypernetworks()
     startup_timer.record("reload hypernetworks")
@@ -373,7 +347,7 @@ def api_only():
     api.launch(
         server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
         port=cmd_opts.port if cmd_opts.port else 7861,
-        root_path = f"/{cmd_opts.subpath}"
+        root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
     )
 
 
@@ -406,7 +380,7 @@ def webui():
             ssl_verify=cmd_opts.disable_tls_verify,
             debug=cmd_opts.gradio_debug,
             auth=gradio_auth_creds,
-            inbrowser=cmd_opts.autolaunch and os.getenv('SD_WEBUI_RESTARTING ') != '1',
+            inbrowser=cmd_opts.autolaunch and os.getenv('SD_WEBUI_RESTARTING') != '1',
             prevent_thread_lock=True,
             allowed_paths=cmd_opts.gradio_allowed_path,
             app_kwargs={

webui.sh

@@ -4,8 +4,15 @@
 # change the variables in webui-user.sh instead #
 #################################################
 
+
+use_venv=1
+if [[ $venv_dir == "-" ]]; then
+  use_venv=0
+fi
+
 SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
 
+
 # If run from macOS, load defaults from webui-macos-env.sh
 if [[ "$OSTYPE" == "darwin"* ]]; then
     if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]]
@@ -47,7 +54,7 @@ then
 fi
 
 # python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-if [[ -z "${venv_dir}" ]]
+if [[ -z "${venv_dir}" ]] && [[ $use_venv -eq 1 ]]
 then
     venv_dir="venv"
 fi
@@ -164,7 +171,7 @@ do
     fi
 done
 
-if ! "${python_cmd}" -c "import venv" &>/dev/null
+if [[ $use_venv -eq 1 ]] && ! "${python_cmd}" -c "import venv" &>/dev/null
 then
     printf "\n%s\n" "${delimiter}"
     printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
@@ -184,7 +191,7 @@ else
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
 
-if [[ -z "${VIRTUAL_ENV}" ]];
+if [[ $use_venv -eq 1 ]] && [[ -z "${VIRTUAL_ENV}" ]];
 then
     printf "\n%s\n" "${delimiter}"
     printf "Create and activate python venv"
@@ -207,7 +214,7 @@ then
     fi
 else
     printf "\n%s\n" "${delimiter}"
-    printf "python venv already activate: ${VIRTUAL_ENV}"
+    printf "python venv already activate or run without venv: ${VIRTUAL_ENV}"
     printf "\n%s\n" "${delimiter}"
 fi