Merge remote-tracking branch 'origin/dev' into soft-inpainting

# Conflicts:
#	modules/processing.py

.eslintrc.js

@@ -74,6 +74,7 @@ module.exports = {
         create_submit_args: "readonly",
         restart_reload: "readonly",
         updateInput: "readonly",
+        onEdit: "readonly",
         //extraNetworks.js
         requestGet: "readonly",
         popup: "readonly",

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,25 +1,45 @@
 name: Bug Report
-description: You think somethings is broken in the UI
+description: You think something is broken in the UI
 title: "[Bug]: "
 labels: ["bug-report"]
 
 body:
-  - type: checkboxes
-    attributes:
-      label: Is there an existing issue for this?
-      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
-      options:
-        - label: I have searched the existing issues and checked the recent builds/commits
-          required: true
   - type: markdown
     attributes:
       value: |
-        *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
+        > The title of the bug report should be short and descriptive.
+        > Use relevant keywords for searchability.
+        > Do not leave it blank, but also do not put an entire error log in it.
+  - type: checkboxes
+    attributes:
+      label: Checklist
+      description: |
+        Please perform basic debugging to see if extensions or configuration is the cause of the issue.
+        Basic debug procedure
+        　1. Disable all third-party extensions - check if extension is the cause
+        　2. Update extensions and webui - sometimes things just need to be updated
+        　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration
+        　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed
+        　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue
+        Before making a issue report please, check that the issue hasn't been reported recently.
+      options:
+        - label: The issue exists after disabling all extensions
+        - label: The issue exists on a clean installation of webui
+        - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui
+        - label: The issue exists in the current version of the webui
+        - label: The issue has not been reported before recently
+        - label: The issue has been reported before but has not been fixed yet
+  - type: markdown
+    attributes:
+      value: |
+        > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible
   - type: textarea
     id: what-did
     attributes:
       label: What happened?
       description: Tell us what happened in a very clear and simple way
+      placeholder: |
+        txt2img is not working as intended.
     validations:
       required: true
   - type: textarea
@@ -27,9 +47,9 @@ body:
     attributes:
       label: Steps to reproduce the problem
       description: Please provide us with precise step by step instructions on how to reproduce the bug
-      value: |
+      placeholder: |
-        1. Go to .... 
+        1. Go to ...
-        2. Press ....
+        2. Press ...
         3. ...
     validations:
       required: true
@@ -38,13 +58,8 @@ body:
     attributes:
       label: What should have happened?
       description: Tell us what you think the normal behavior should be
-    validations:
+      placeholder: |
-      required: true
+        WebUI should ...
-  - type: textarea
-    id: sysinfo
-    attributes:
-      label: Sysinfo
-      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
     validations:
       required: true
   - type: dropdown
@@ -58,12 +73,25 @@ body:
         - Brave
         - Apple Safari
         - Microsoft Edge
+        - Android
+        - iOS
         - Other
+  - type: textarea
+    id: sysinfo
+    attributes:
+      label: Sysinfo
+      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
+      placeholder: |
+        1. Go to WebUI Settings -> Sysinfo -> Download system info.
+            If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file
+        2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text.
+    validations:
+      required: true
   - type: textarea
     id: logs
     attributes:
       label: Console logs
-      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
+      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
       render: Shell
     validations:
       required: true
@@ -71,4 +99,7 @@ body:
     id: misc
     attributes:
       label: Additional information
-      description: Please provide us with any relevant additional info or context.
+      description: | 
+        Please provide us with any relevant additional info or context.
+        Examples:
+        　I have updated my GPU driver recently.

.github/workflows/on_pull_request.yaml

@@ -20,7 +20,7 @@ jobs:
           #     not to have GHA download an (at the time of writing) 4 GB cache
           #     of PyTorch and other dependencies.
       - name: Install Ruff
-        run: pip install ruff==0.0.272
+        run: pip install ruff==0.1.6
       - name: Run Ruff
         run: ruff .
   lint-js:

README.md

@@ -88,9 +88,10 @@ 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 dimension must be a multiple of 8 rather than 64
+- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
+- [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
@@ -103,7 +104,7 @@ Alternatively, use online services (like Google Colab):
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
 ### Installation on Windows 10/11 with NVidia-GPUs using release package
-1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents.
 2. Run `update.bat`.
 3. Run `run.bat`.
 > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
@@ -120,7 +121,9 @@ Alternatively, use online services (like Google Colab):
 # Debian-based:
 sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
 # Red Hat-based:
-sudo dnf install wget git python3
+sudo dnf install wget git python3 gperftools-libs libglvnd-glx 
+# openSUSE-based:
+sudo zypper install wget git python3 libtcmalloc4 libglvnd
 # Arch-based:
 sudo pacman -S wget git python3
 ```
@@ -146,7 +149,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
 ## Credits
 Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
 
-- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
+- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
 - CodeFormer - https://github.com/sczhou/CodeFormer
@@ -173,5 +176,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
 - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
+- Hypertile - tfernd - https://github.com/tfernd/HyperTile
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

configs/alt-diffusion-m18-inference.yaml

@@ -0,0 +1,73 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: modules.xlmr_m18.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"

extensions-builtin/Lora/lora_logger.py

@@ -0,0 +1,33 @@
+import sys
+import copy
+import logging
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+logger = logging.getLogger("lora")
+logger.propagate = False
+
+
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(
+        ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
+    )
+    logger.addHandler(handler)

extensions-builtin/Lora/lyco_helpers.py

@@ -19,3 +19,50 @@ 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)
+
+
+# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
+def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
+    '''
+    return a tuple of two value of input dimension decomposed by the number closest to factor
+    second value is higher or equal than first value.
+
+    In LoRA with Kroneckor Product, first value is a value for weight scale.
+    secon value is a value for weight.
+
+    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
+
+    examples)
+    factor
+        -1               2                4               8               16               ...
+    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
+    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
+    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
+    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
+    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
+    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
+    '''
+
+    if factor > 0 and (dimension % factor) == 0:
+        m = factor
+        n = dimension // factor
+        if m > n:
+            n, m = m, n
+        return m, n
+    if factor < 0:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m<n:
+        new_m = m + 1
+        while dimension%new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m>factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n
+

extensions-builtin/Lora/network.py

@@ -93,6 +93,7 @@ class Network:  # LoraModule
         self.unet_multiplier = 1.0
         self.dyn_dim = None
         self.modules = {}
+        self.bundle_embeddings = {}
         self.mtime = None
 
         self.mentioned_name = None

extensions-builtin/Lora/network_glora.py

@@ -0,0 +1,33 @@
+
+import network
+
+class ModuleTypeGLora(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
+            return NetworkModuleGLora(net, weights)
+
+        return None
+
+# adapted from https://github.com/KohakuBlueleaf/LyCORIS
+class NetworkModuleGLora(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["a1.weight"]
+        self.w1b = weights.w["b1.weight"]
+        self.w2a = weights.w["a2.weight"]
+        self.w2b = weights.w["b2.weight"]
+
+    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)]
+        updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_oft.py

@@ -0,0 +1,97 @@
+import torch
+import network
+from lyco_helpers import factorization
+from einops import rearrange
+
+
+class ModuleTypeOFT(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
+            return NetworkModuleOFT(net, weights)
+
+        return None
+
+# Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
+# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
+class NetworkModuleOFT(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+
+        super().__init__(net, weights)
+
+        self.lin_module = None
+        self.org_module: list[torch.Module] = [self.sd_module]
+
+        # kohya-ss
+        if "oft_blocks" in weights.w.keys():
+            self.is_kohya = True
+            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
+            self.alpha = weights.w["alpha"] # alpha is constraint
+            self.dim = self.oft_blocks.shape[0] # lora dim
+        # LyCORIS
+        elif "oft_diag" in weights.w.keys():
+            self.is_kohya = False
+            self.oft_blocks = weights.w["oft_diag"]
+            # self.alpha is unused
+            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
+
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
+        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
+        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
+
+        if is_linear:
+            self.out_dim = self.sd_module.out_features
+        elif is_conv:
+            self.out_dim = self.sd_module.out_channels
+        elif is_other_linear:
+            self.out_dim = self.sd_module.embed_dim
+
+        if self.is_kohya:
+            self.constraint = self.alpha * self.out_dim
+            self.num_blocks = self.dim
+            self.block_size = self.out_dim // self.dim
+        else:
+            self.constraint = None
+            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
+
+    def calc_updown_kb(self, orig_weight, multiplier):
+        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+
+        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
+
+        # This errors out for MultiheadAttention, might need to be handled up-stream
+        merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
+        merged_weight = torch.einsum(
+            'k n m, k n ... -> k m ...',
+            R,
+            merged_weight
+        )
+        merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
+
+        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        output_shape = orig_weight.shape
+        return self.finalize_updown(updown, orig_weight, output_shape)
+
+    def calc_updown(self, orig_weight):
+        # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
+        multiplier = self.multiplier()
+        return self.calc_updown_kb(orig_weight, multiplier)
+
+    # override to remove the multiplier/scale factor; it's already multiplied in get_weight
+    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
+        if self.bias is not None:
+            updown = updown.reshape(self.bias.shape)
+            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            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)
+
+        if ex_bias is not None:
+            ex_bias = ex_bias * self.multiplier()
+
+        return updown, ex_bias

extensions-builtin/Lora/networks.py

@@ -5,16 +5,21 @@ import re
 import lora_patches
 import network
 import network_lora
+import network_glora
 import network_hada
 import network_ia3
 import network_lokr
 import network_full
 import network_norm
+import network_oft
 
 import torch
 from typing import Union
 
 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
+import modules.textual_inversion.textual_inversion as textual_inversion
+
+from lora_logger import logger
 
 module_types = [
     network_lora.ModuleTypeLora(),
@@ -23,6 +28,8 @@ module_types = [
     network_lokr.ModuleTypeLokr(),
     network_full.ModuleTypeFull(),
     network_norm.ModuleTypeNorm(),
+    network_glora.ModuleTypeGLora(),
+    network_oft.ModuleTypeOFT(),
 ]
 
 
@@ -149,9 +156,19 @@ def load_network(name, network_on_disk):
     is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
 
     matched_networks = {}
+    bundle_embeddings = {}
 
     for key_network, weight in sd.items():
         key_network_without_network_parts, network_part = key_network.split(".", 1)
+        if key_network_without_network_parts == "bundle_emb":
+            emb_name, vec_name = network_part.split(".", 1)
+            emb_dict = bundle_embeddings.get(emb_name, {})
+            if vec_name.split('.')[0] == 'string_to_param':
+                _, k2 = vec_name.split('.', 1)
+                emb_dict['string_to_param'] = {k2: weight}
+            else:
+                emb_dict[vec_name] = weight
+            bundle_embeddings[emb_name] = emb_dict
 
         key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
         sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@@ -174,6 +191,17 @@ def load_network(name, network_on_disk):
                 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)
 
+        # kohya_ss OFT module
+        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        # KohakuBlueLeaf OFT module
+        if sd_module is None and "oft_diag" in key:
+            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+            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)
+
         if sd_module is None:
             keys_failed_to_match[key_network] = key
             continue
@@ -195,6 +223,14 @@ def load_network(name, network_on_disk):
 
         net.modules[key] = net_module
 
+    embeddings = {}
+    for emb_name, data in bundle_embeddings.items():
+        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
+        embedding.loaded = None
+        embeddings[emb_name] = embedding
+
+    net.bundle_embeddings = embeddings
+
     if keys_failed_to_match:
         logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
 
@@ -210,11 +246,15 @@ def purge_networks_from_memory():
 
 
 def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+    emb_db = sd_hijack.model_hijack.embedding_db
     already_loaded = {}
 
     for net in loaded_networks:
         if net.name in names:
             already_loaded[net.name] = net
+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded:
+                emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)
 
     loaded_networks.clear()
 
@@ -257,6 +297,21 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
         net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
         loaded_networks.append(net)
 
+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded is None and emb_name in emb_db.word_embeddings:
+                logger.warning(
+                    f'Skip bundle embedding: "{emb_name}"'
+                    ' as it was already loaded from embeddings folder'
+                )
+                continue
+
+            embedding.loaded = False
+            if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
+                embedding.loaded = True
+                emb_db.register_embedding(embedding, shared.sd_model)
+            else:
+                emb_db.skipped_embeddings[name] = embedding
+
     if failed_to_load_networks:
         sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
 
@@ -418,6 +473,7 @@ def network_forward(module, input, original_forward):
 def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
     self.network_current_names = ()
     self.network_weights_backup = None
+    self.network_bias_backup = None
 
 
 def network_Linear_forward(self, input):
@@ -564,6 +620,7 @@ extra_network_lora = None
 available_networks = {}
 available_network_aliases = {}
 loaded_networks = []
+loaded_bundle_embeddings = {}
 networks_in_memory = {}
 available_network_hash_lookup = {}
 forbidden_network_aliases = {}

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -17,6 +17,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
 
     def create_item(self, name, index=None, enable_filter=True):
         lora_on_disk = networks.available_networks.get(name)
+        if lora_on_disk is None:
+            return
 
         path, ext = os.path.splitext(lora_on_disk.filename)
 
@@ -66,9 +68,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         return item
 
     def list_items(self):
-        for index, name in enumerate(networks.available_networks):
+        # instantiate a list to protect against concurrent modification
+        names = list(networks.available_networks)
+        for index, name in enumerate(names):
             item = self.create_item(name, index)
-
             if item is not None:
                 yield item
 

extensions-builtin/hypertile/hypertile.py

@@ -0,0 +1,345 @@
+"""
+Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
+Warn: The patch works well only if the input image has a width and height that are multiples of 128
+Original author: @tfernd Github: https://github.com/tfernd/HyperTile
+"""
+
+from __future__ import annotations
+
+import functools
+from dataclasses import dataclass
+from typing import Callable
+
+from functools import wraps, cache
+
+import math
+import torch.nn as nn
+import random
+
+from einops import rearrange
+
+
+@dataclass
+class HypertileParams:
+    depth = 0
+    layer_name = ""
+    tile_size: int = 0
+    swap_size: int = 0
+    aspect_ratio: float = 1.0
+    forward = None
+    enabled = False
+
+
+
+# TODO add SD-XL layers
+DEPTH_LAYERS = {
+    0: [
+        # SD 1.5 U-Net (diffusers)
+        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
+        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "input_blocks.1.1.transformer_blocks.0.attn1",
+        "input_blocks.2.1.transformer_blocks.0.attn1",
+        "output_blocks.9.1.transformer_blocks.0.attn1",
+        "output_blocks.10.1.transformer_blocks.0.attn1",
+        "output_blocks.11.1.transformer_blocks.0.attn1",
+        # SD 1.5 VAE
+        "decoder.mid_block.attentions.0",
+        "decoder.mid.attn_1",
+    ],
+    1: [
+        # SD 1.5 U-Net (diffusers)
+        "down_blocks.1.attentions.0.transformer_blocks.0.attn1",
+        "down_blocks.1.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.2.attentions.0.transformer_blocks.0.attn1",
+        "up_blocks.2.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.2.attentions.2.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "input_blocks.4.1.transformer_blocks.0.attn1",
+        "input_blocks.5.1.transformer_blocks.0.attn1",
+        "output_blocks.6.1.transformer_blocks.0.attn1",
+        "output_blocks.7.1.transformer_blocks.0.attn1",
+        "output_blocks.8.1.transformer_blocks.0.attn1",
+    ],
+    2: [
+        # SD 1.5 U-Net (diffusers)
+        "down_blocks.2.attentions.0.transformer_blocks.0.attn1",
+        "down_blocks.2.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.1.attentions.0.transformer_blocks.0.attn1",
+        "up_blocks.1.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.1.attentions.2.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "input_blocks.7.1.transformer_blocks.0.attn1",
+        "input_blocks.8.1.transformer_blocks.0.attn1",
+        "output_blocks.3.1.transformer_blocks.0.attn1",
+        "output_blocks.4.1.transformer_blocks.0.attn1",
+        "output_blocks.5.1.transformer_blocks.0.attn1",
+    ],
+    3: [
+        # SD 1.5 U-Net (diffusers)
+        "mid_block.attentions.0.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "middle_block.1.transformer_blocks.0.attn1",
+    ],
+}
+# XL layers, thanks for GitHub@gel-crabs for the help
+DEPTH_LAYERS_XL = {
+    0: [
+        # SD 1.5 U-Net (diffusers)
+        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
+        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
+        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "input_blocks.4.1.transformer_blocks.0.attn1",
+        "input_blocks.5.1.transformer_blocks.0.attn1",
+        "output_blocks.3.1.transformer_blocks.0.attn1",
+        "output_blocks.4.1.transformer_blocks.0.attn1",
+        "output_blocks.5.1.transformer_blocks.0.attn1",
+        # SD 1.5 VAE
+        "decoder.mid_block.attentions.0",
+        "decoder.mid.attn_1",
+    ],
+    1: [
+        # SD 1.5 U-Net (diffusers)
+        #"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
+        #"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
+        #"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
+        #"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
+        #"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "input_blocks.4.1.transformer_blocks.1.attn1",
+        "input_blocks.5.1.transformer_blocks.1.attn1",
+        "output_blocks.3.1.transformer_blocks.1.attn1",
+        "output_blocks.4.1.transformer_blocks.1.attn1",
+        "output_blocks.5.1.transformer_blocks.1.attn1",
+        "input_blocks.7.1.transformer_blocks.0.attn1",
+        "input_blocks.8.1.transformer_blocks.0.attn1",
+        "output_blocks.0.1.transformer_blocks.0.attn1",
+        "output_blocks.1.1.transformer_blocks.0.attn1",
+        "output_blocks.2.1.transformer_blocks.0.attn1",
+        "input_blocks.7.1.transformer_blocks.1.attn1",
+        "input_blocks.8.1.transformer_blocks.1.attn1",
+        "output_blocks.0.1.transformer_blocks.1.attn1",
+        "output_blocks.1.1.transformer_blocks.1.attn1",
+        "output_blocks.2.1.transformer_blocks.1.attn1",
+        "input_blocks.7.1.transformer_blocks.2.attn1",
+        "input_blocks.8.1.transformer_blocks.2.attn1",
+        "output_blocks.0.1.transformer_blocks.2.attn1",
+        "output_blocks.1.1.transformer_blocks.2.attn1",
+        "output_blocks.2.1.transformer_blocks.2.attn1",
+        "input_blocks.7.1.transformer_blocks.3.attn1",
+        "input_blocks.8.1.transformer_blocks.3.attn1",
+        "output_blocks.0.1.transformer_blocks.3.attn1",
+        "output_blocks.1.1.transformer_blocks.3.attn1",
+        "output_blocks.2.1.transformer_blocks.3.attn1",
+        "input_blocks.7.1.transformer_blocks.4.attn1",
+        "input_blocks.8.1.transformer_blocks.4.attn1",
+        "output_blocks.0.1.transformer_blocks.4.attn1",
+        "output_blocks.1.1.transformer_blocks.4.attn1",
+        "output_blocks.2.1.transformer_blocks.4.attn1",
+        "input_blocks.7.1.transformer_blocks.5.attn1",
+        "input_blocks.8.1.transformer_blocks.5.attn1",
+        "output_blocks.0.1.transformer_blocks.5.attn1",
+        "output_blocks.1.1.transformer_blocks.5.attn1",
+        "output_blocks.2.1.transformer_blocks.5.attn1",
+        "input_blocks.7.1.transformer_blocks.6.attn1",
+        "input_blocks.8.1.transformer_blocks.6.attn1",
+        "output_blocks.0.1.transformer_blocks.6.attn1",
+        "output_blocks.1.1.transformer_blocks.6.attn1",
+        "output_blocks.2.1.transformer_blocks.6.attn1",
+        "input_blocks.7.1.transformer_blocks.7.attn1",
+        "input_blocks.8.1.transformer_blocks.7.attn1",
+        "output_blocks.0.1.transformer_blocks.7.attn1",
+        "output_blocks.1.1.transformer_blocks.7.attn1",
+        "output_blocks.2.1.transformer_blocks.7.attn1",
+        "input_blocks.7.1.transformer_blocks.8.attn1",
+        "input_blocks.8.1.transformer_blocks.8.attn1",
+        "output_blocks.0.1.transformer_blocks.8.attn1",
+        "output_blocks.1.1.transformer_blocks.8.attn1",
+        "output_blocks.2.1.transformer_blocks.8.attn1",
+        "input_blocks.7.1.transformer_blocks.9.attn1",
+        "input_blocks.8.1.transformer_blocks.9.attn1",
+        "output_blocks.0.1.transformer_blocks.9.attn1",
+        "output_blocks.1.1.transformer_blocks.9.attn1",
+        "output_blocks.2.1.transformer_blocks.9.attn1",
+    ],
+    2: [
+        # SD 1.5 U-Net (diffusers)
+        "mid_block.attentions.0.transformer_blocks.0.attn1",
+        # SD 1.5 U-Net (ldm)
+        "middle_block.1.transformer_blocks.0.attn1",
+        "middle_block.1.transformer_blocks.1.attn1",
+        "middle_block.1.transformer_blocks.2.attn1",
+        "middle_block.1.transformer_blocks.3.attn1",
+        "middle_block.1.transformer_blocks.4.attn1",
+        "middle_block.1.transformer_blocks.5.attn1",
+        "middle_block.1.transformer_blocks.6.attn1",
+        "middle_block.1.transformer_blocks.7.attn1",
+        "middle_block.1.transformer_blocks.8.attn1",
+        "middle_block.1.transformer_blocks.9.attn1",
+    ],
+    3 : [] # TODO - separate layers for SD-XL
+}
+
+
+RNG_INSTANCE = random.Random()
+
+
+def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
+    """
+    Returns a random divisor of value that
+        x * min_value <= value
+    if max_options is 1, the behavior is deterministic
+    """
+    min_value = min(min_value, value)
+
+    # All big divisors of value (inclusive)
+    divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
+
+    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element # big -> small order
+
+    idx = RNG_INSTANCE.randint(0, len(ns) - 1)
+
+    return ns[idx]
+
+
+def set_hypertile_seed(seed: int) -> None:
+    RNG_INSTANCE.seed(seed)
+
+
+@functools.cache
+def largest_tile_size_available(width: int, height: int) -> int:
+    """
+    Calculates the largest tile size available for a given width and height
+    Tile size is always a power of 2
+    """
+    gcd = math.gcd(width, height)
+    largest_tile_size_available = 1
+    while gcd % (largest_tile_size_available * 2) == 0:
+        largest_tile_size_available *= 2
+    return largest_tile_size_available
+
+
+def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
+    """
+    Finds h and w such that h*w = hw and h/w = aspect_ratio
+    We check all possible divisors of hw and return the closest to the aspect ratio
+    """
+    divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
+    pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
+    ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
+    closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
+    closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
+    return closest_pair
+
+
+@cache
+def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
+    """
+    Finds h and w such that h*w = hw and h/w = aspect_ratio
+    """
+    h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
+    # find h and w such that h*w = hw and h/w = aspect_ratio
+    if h * w != hw:
+        w_candidate = hw / h
+        # check if w is an integer
+        if not w_candidate.is_integer():
+            h_candidate = hw / w
+            # check if h is an integer
+            if not h_candidate.is_integer():
+                return iterative_closest_divisors(hw, aspect_ratio)
+            else:
+                h = int(h_candidate)
+        else:
+            w = int(w_candidate)
+    return h, w
+
+
+def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
+
+    @wraps(params.forward)
+    def wrapper(*args, **kwargs):
+        if not params.enabled:
+            return params.forward(*args, **kwargs)
+
+        latent_tile_size = max(128, params.tile_size) // 8
+        x = args[0]
+
+        # VAE
+        if x.ndim == 4:
+            b, c, h, w = x.shape
+
+            nh = random_divisor(h, latent_tile_size, params.swap_size)
+            nw = random_divisor(w, latent_tile_size, params.swap_size)
+
+            if nh * nw > 1:
+                x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw)  # split into nh * nw tiles
+
+            out = params.forward(x, *args[1:], **kwargs)
+
+            if nh * nw > 1:
+                out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
+
+        # U-Net
+        else:
+            hw: int = x.size(1)
+            h, w = find_hw_candidates(hw, params.aspect_ratio)
+            assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
+
+            factor = 2 ** params.depth if scale_depth else 1
+            nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
+            nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
+
+            if nh * nw > 1:
+                x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
+
+            out = params.forward(x, *args[1:], **kwargs)
+
+            if nh * nw > 1:
+                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
+                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
+
+        return out
+
+    return wrapper
+
+
+def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
+    hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
+    if hypertile_layers is None:
+        if not enable:
+            return
+
+        hypertile_layers = {}
+        layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
+
+        for depth in range(4):
+            for layer_name, module in model.named_modules():
+                if any(layer_name.endswith(try_name) for try_name in layers[depth]):
+                    params = HypertileParams()
+                    module.__webui_hypertile_params = params
+                    params.forward = module.forward
+                    params.depth = depth
+                    params.layer_name = layer_name
+                    module.forward = self_attn_forward(params)
+
+                    hypertile_layers[layer_name] = 1
+
+        model.__webui_hypertile_layers = hypertile_layers
+
+    aspect_ratio = width / height
+    tile_size = min(largest_tile_size_available(width, height), tile_size_max)
+
+    for layer_name, module in model.named_modules():
+        if layer_name in hypertile_layers:
+            params = module.__webui_hypertile_params
+
+            params.tile_size = tile_size
+            params.swap_size = swap_size
+            params.aspect_ratio = aspect_ratio
+            params.enabled = enable and params.depth <= max_depth

extensions-builtin/hypertile/scripts/hypertile_script.py

@@ -0,0 +1,73 @@
+import hypertile
+from modules import scripts, script_callbacks, shared
+
+
+class ScriptHypertile(scripts.Script):
+    name = "Hypertile"
+
+    def title(self):
+        return self.name
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def process(self, p, *args):
+        hypertile.set_hypertile_seed(p.all_seeds[0])
+
+        configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
+
+    def before_hr(self, p, *args):
+        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet)
+
+
+def configure_hypertile(width, height, enable_unet=True):
+    hypertile.hypertile_hook_model(
+        shared.sd_model.first_stage_model,
+        width,
+        height,
+        swap_size=shared.opts.hypertile_swap_size_vae,
+        max_depth=shared.opts.hypertile_max_depth_vae,
+        tile_size_max=shared.opts.hypertile_max_tile_vae,
+        enable=shared.opts.hypertile_enable_vae,
+    )
+
+    hypertile.hypertile_hook_model(
+        shared.sd_model.model,
+        width,
+        height,
+        swap_size=shared.opts.hypertile_swap_size_unet,
+        max_depth=shared.opts.hypertile_max_depth_unet,
+        tile_size_max=shared.opts.hypertile_max_tile_unet,
+        enable=enable_unet,
+        is_sdxl=shared.sd_model.is_sdxl
+    )
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    options = {
+        "hypertile_explanation": shared.OptionHTML("""
+    <a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
+    resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
+    benefit.
+    """),
+
+        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
+        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
+        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
+        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+
+        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
+        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
+        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
+    }
+
+    for name, opt in options.items():
+        opt.section = ('hypertile', "Hypertile")
+        shared.opts.add_option(name, opt)
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/mobile/javascript/mobile.js

@@ -12,6 +12,8 @@ function isMobile() {
 }
 
 function reportWindowSize() {
+    if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout
+
     var currentlyMobile = isMobile();
     if (currentlyMobile == isSetupForMobile) return;
     isSetupForMobile = currentlyMobile;

javascript/dragdrop.js

@@ -119,7 +119,7 @@ window.addEventListener('paste', e => {
     }
 
     const firstFreeImageField = visibleImageFields
-        .filter(el => el.querySelector('input[type=file]'))?.[0];
+        .filter(el => !el.querySelector('img'))?.[0];
 
     dropReplaceImage(
         firstFreeImageField ?

javascript/edit-attention.js

@@ -18,37 +18,43 @@ function keyupEditAttention(event) {
         const before = text.substring(0, selectionStart);
         let beforeParen = before.lastIndexOf(OPEN);
         if (beforeParen == -1) return false;
-        let beforeParenClose = before.lastIndexOf(CLOSE);
+
-        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+        let beforeClosingParen = before.lastIndexOf(CLOSE);
-            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
+        if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false;
-            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
-        }
 
         // Find closing parenthesis around current cursor
         const after = text.substring(selectionStart);
         let afterParen = after.indexOf(CLOSE);
         if (afterParen == -1) return false;
-        let afterParenOpen = after.indexOf(OPEN);
+
-        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+        let afterOpeningParen = after.indexOf(OPEN);
-            afterParen = after.indexOf(CLOSE, afterParen + 1);
+        if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false;
-            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
-        }
-        if (beforeParen === -1 || afterParen === -1) return false;
 
         // Set the selection to the text between the parenthesis
         const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
-        const lastColon = parenContent.lastIndexOf(":");
+        if (/.*:-?[\d.]+/s.test(parenContent)) {
-        selectionStart = beforeParen + 1;
+            const lastColon = parenContent.lastIndexOf(":");
-        selectionEnd = selectionStart + lastColon;
+            selectionStart = beforeParen + 1;
+            selectionEnd = selectionStart + lastColon;
+        } else {
+            selectionStart = beforeParen + 1;
+            selectionEnd = selectionStart + parenContent.length;
+        }
+
         target.setSelectionRange(selectionStart, selectionEnd);
         return true;
     }
 
     function selectCurrentWord() {
         if (selectionStart !== selectionEnd) return false;
-        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"};
+        let delimiters = opts.keyedit_delimiters;
 
-        // seek backward until to find beggining
+        for (let i of opts.keyedit_delimiters_whitespace) {
+            delimiters += whitespace_delimiters[i];
+        }
+
+        // seek backward to find beginning
         while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
             selectionStart--;
         }
@@ -63,7 +69,7 @@ function keyupEditAttention(event) {
     }
 
     // If the user hasn't selected anything, let's select their current parenthesis block or word
-    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) {
         selectCurrentWord();
     }
 
@@ -71,33 +77,54 @@ function keyupEditAttention(event) {
 
     var closeCharacter = ')';
     var delta = opts.keyedit_precision_attention;
+    var start = selectionStart > 0 ? text[selectionStart - 1] : "";
+    var end = text[selectionEnd];
 
-    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
+    if (start == '<') {
         closeCharacter = '>';
         delta = opts.keyedit_precision_extra;
-    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
+    } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis)))
+        let numParen = 0;
 
+        while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) {
+            numParen++;
+        }
+
+        if (start == "[") {
+            weight = (1 / 1.1) ** numParen;
+        } else {
+            weight = 1.1 ** numParen;
+        }
+
+        weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention;
+
+        text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen);
+        selectionStart -= numParen - 1;
+        selectionEnd -= numParen - 1;
+    } else if (start != '(') {
         // do not include spaces at the end
         while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
-            selectionEnd -= 1;
+            selectionEnd--;
         }
+
         if (selectionStart == selectionEnd) {
             return;
         }
 
         text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
 
-        selectionStart += 1;
+        selectionStart++;
-        selectionEnd += 1;
+        selectionEnd++;
     }
 
-    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    if (text[selectionEnd] != ':') return;
-    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+    var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength));
     if (isNaN(weight)) return;
 
     weight += isPlus ? delta : -delta;
     weight = parseFloat(weight.toPrecision(12));
-    if (String(weight).length == 1) weight += ".0";
+    if (Number.isInteger(weight)) weight += ".0";
 
     if (closeCharacter == ')' && weight == 1) {
         var endParenPos = text.substring(selectionEnd).indexOf(')');
@@ -105,7 +132,7 @@ function keyupEditAttention(event) {
         selectionStart--;
         selectionEnd--;
     } else {
-        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength);
     }
 
     target.focus();

javascript/extraNetworks.js

@@ -26,8 +26,9 @@ function setupExtraNetworksForTab(tabname) {
     var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
     var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
     var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
+    var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container');
+    var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt');
 
-    sort.dataset.sortkey = 'sortDefault';
     tabs.appendChild(searchDiv);
     tabs.appendChild(sort);
     tabs.appendChild(sortOrder);
@@ -49,20 +50,23 @@ function setupExtraNetworksForTab(tabname) {
 
             elem.style.display = visible ? "" : "none";
         });
+
+        applySort();
     };
 
     var applySort = function() {
+        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
+
         var reverse = sortOrder.classList.contains("sortReverse");
-        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
+        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name";
-        sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
+        sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
-        var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
+        var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
-        if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
+
+        if (sortKeyStore == sort.dataset.sortkey) {
             return;
         }
-
         sort.dataset.sortkey = sortKeyStore;
 
-        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
         cards.forEach(function(card) {
             card.originalParentElement = card.parentElement;
         });
@@ -88,15 +92,13 @@ function setupExtraNetworksForTab(tabname) {
     };
 
     search.addEventListener("input", applyFilter);
-    applyFilter();
-    ["change", "blur", "click"].forEach(function(evt) {
-        sort.querySelector("input").addEventListener(evt, applySort);
-    });
     sortOrder.addEventListener("click", function() {
         sortOrder.classList.toggle("sortReverse");
         applySort();
     });
+    applyFilter();
 
+    extraNetworksApplySort[tabname] = applySort;
     extraNetworksApplyFilter[tabname] = applyFilter;
 
     var showDirsUpdate = function() {
@@ -109,11 +111,51 @@ function setupExtraNetworksForTab(tabname) {
     showDirsUpdate();
 }
 
+function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) {
+    if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout
+
+    var promptContainer = gradioApp().getElementById(tabname + '_prompt_container');
+    var prompt = gradioApp().getElementById(tabname + '_prompt_row');
+    var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row');
+    var elem = id ? gradioApp().getElementById(id) : null;
+
+    if (showNegativePrompt && elem) {
+        elem.insertBefore(negPrompt, elem.firstChild);
+    } else {
+        promptContainer.insertBefore(negPrompt, promptContainer.firstChild);
+    }
+
+    if (showPrompt && elem) {
+        elem.insertBefore(prompt, elem.firstChild);
+    } else {
+        promptContainer.insertBefore(prompt, promptContainer.firstChild);
+    }
+
+    if (elem) {
+        elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
+    }
+}
+
+
+function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
+    extraNetworksMovePromptToTab(tabname, '', false, false);
+}
+
+function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
+    extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
+
+}
+
 function applyExtraNetworkFilter(tabname) {
     setTimeout(extraNetworksApplyFilter[tabname], 1);
 }
 
+function applyExtraNetworkSort(tabname) {
+    setTimeout(extraNetworksApplySort[tabname], 1);
+}
+
 var extraNetworksApplyFilter = {};
+var extraNetworksApplySort = {};
 var activePromptTextarea = {};
 
 function setupExtraNetworks() {
@@ -140,14 +182,15 @@ function setupExtraNetworks() {
 
 onUiLoaded(setupExtraNetworks);
 
-var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
+var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
-var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
+var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
 
 function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
     var m = text.match(re_extranet);
     var replaced = false;
     var newTextareaText;
     if (m) {
+        var extraTextBeforeNet = opts.extra_networks_add_text_separator;
         var extraTextAfterNet = m[2];
         var partToSearch = m[1];
         var foundAtPosition = -1;
@@ -161,8 +204,13 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
             return found;
         });
 
-        if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+        if (foundAtPosition >= 0) {
-            newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
+            if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+                newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
+            }
+            if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
+                newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition);
+            }
         }
     } else {
         newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
@@ -216,27 +264,24 @@ function extraNetworksSearchButton(tabs_id, event) {
 
 var globalPopup = null;
 var globalPopupInner = null;
+
 function closePopup() {
     if (!globalPopup) return;
-
     globalPopup.style.display = "none";
 }
+
 function popup(contents) {
     if (!globalPopup) {
         globalPopup = document.createElement('div');
-        globalPopup.onclick = closePopup;
         globalPopup.classList.add('global-popup');
 
         var close = document.createElement('div');
         close.classList.add('global-popup-close');
-        close.onclick = closePopup;
+        close.addEventListener("click", closePopup);
         close.title = "Close";
         globalPopup.appendChild(close);
 
         globalPopupInner = document.createElement('div');
-        globalPopupInner.onclick = function(event) {
-            event.stopPropagation(); return false;
-        };
         globalPopupInner.classList.add('global-popup-inner');
         globalPopup.appendChild(globalPopupInner);
 
@@ -335,7 +380,7 @@ function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
 function extraNetworksRefreshSingleCard(page, tabname, name) {
     requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
         if (data && data.html) {
-            var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
+            var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`);
 
             var newDiv = document.createElement('DIV');
             newDiv.innerHTML = data.html;

javascript/imageviewer.js

@@ -33,8 +33,11 @@ function updateOnBackgroundChange() {
     const modalImage = gradioApp().getElementById("modalImage");
     if (modalImage && modalImage.offsetParent) {
         let currentButton = selected_gallery_button();
-
+        let preview = gradioApp().querySelectorAll('.livePreview > img');
-        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+        if (preview.length > 0) {
+            // show preview image if available
+            modalImage.src = preview[preview.length - 1].src;
+        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
             if (modalImage.style.display === 'none') {
                 const modal = gradioApp().getElementById("lightboxModal");

javascript/inputAccordion.js

@@ -1,37 +1,68 @@
-var observerAccordionOpen = new MutationObserver(function(mutations) {
-    mutations.forEach(function(mutationRecord) {
-        var elem = mutationRecord.target;
-        var open = elem.classList.contains('open');
-
-        var accordion = elem.parentNode;
-        accordion.classList.toggle('input-accordion-open', open);
-
-        var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
-        checkbox.checked = open;
-        updateInput(checkbox);
-
-        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
-        if (extra) {
-            extra.style.display = open ? "" : "none";
-        }
-    });
-});
-
 function inputAccordionChecked(id, checked) {
-    var label = gradioApp().querySelector('#' + id + " .label-wrap");
+    var accordion = gradioApp().getElementById(id);
-    if (label.classList.contains('open') != checked) {
+    accordion.visibleCheckbox.checked = checked;
-        label.click();
+    accordion.onVisibleCheckboxChange();
+}
+
+function setupAccordion(accordion) {
+    var labelWrap = accordion.querySelector('.label-wrap');
+    var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
+    var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+    var span = labelWrap.querySelector('span');
+    var linked = true;
+
+    var isOpen = function() {
+        return labelWrap.classList.contains('open');
+    };
+
+    var observerAccordionOpen = new MutationObserver(function(mutations) {
+        mutations.forEach(function(mutationRecord) {
+            accordion.classList.toggle('input-accordion-open', isOpen());
+
+            if (linked) {
+                accordion.visibleCheckbox.checked = isOpen();
+                accordion.onVisibleCheckboxChange();
+            }
+        });
+    });
+    observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
+
+    if (extra) {
+        labelWrap.insertBefore(extra, labelWrap.lastElementChild);
     }
+
+    accordion.onChecked = function(checked) {
+        if (isOpen() != checked) {
+            labelWrap.click();
+        }
+    };
+
+    var visibleCheckbox = document.createElement('INPUT');
+    visibleCheckbox.type = 'checkbox';
+    visibleCheckbox.checked = isOpen();
+    visibleCheckbox.id = accordion.id + "-visible-checkbox";
+    visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
+    span.insertBefore(visibleCheckbox, span.firstChild);
+
+    accordion.visibleCheckbox = visibleCheckbox;
+    accordion.onVisibleCheckboxChange = function() {
+        if (linked && isOpen() != visibleCheckbox.checked) {
+            labelWrap.click();
+        }
+
+        gradioCheckbox.checked = visibleCheckbox.checked;
+        updateInput(gradioCheckbox);
+    };
+
+    visibleCheckbox.addEventListener('click', function(event) {
+        linked = false;
+        event.stopPropagation();
+    });
+    visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange);
 }
 
 onUiLoaded(function() {
     for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
-        var labelWrap = accordion.querySelector('.label-wrap');
+        setupAccordion(accordion);
-        observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
-
-        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
-        if (extra) {
-            labelWrap.insertBefore(extra, labelWrap.lastElementChild);
-        }
     }
 });

javascript/notification.js

@@ -26,7 +26,11 @@ onAfterUiUpdate(function() {
     lastHeadImg = headImg;
 
     // play notification sound if available
-    gradioApp().querySelector('#audio_notification audio')?.play();
+    const notificationAudio = gradioApp().querySelector('#audio_notification audio');
+    if (notificationAudio) {
+        notificationAudio.volume = opts.notification_volume / 100.0 || 1.0;
+        notificationAudio.play();
+    }
 
     if (document.hasFocus()) return;
 

javascript/settings.js

@@ -0,0 +1,71 @@
+let settingsExcludeTabsFromShowAll = {
+    settings_tab_defaults: 1,
+    settings_tab_sysinfo: 1,
+    settings_tab_actions: 1,
+    settings_tab_licenses: 1,
+};
+
+function settingsShowAllTabs() {
+    gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
+        if (settingsExcludeTabsFromShowAll[elem.id]) return;
+
+        elem.style.display = "block";
+    });
+}
+
+function settingsShowOneTab() {
+    gradioApp().querySelector('#settings_show_one_page').click();
+}
+
+onUiLoaded(function() {
+    var edit = gradioApp().querySelector('#settings_search');
+    var editTextarea = gradioApp().querySelector('#settings_search > label > input');
+    var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages');
+    var settings_tabs = gradioApp().querySelector('#settings div');
+
+    onEdit('settingsSearch', editTextarea, 250, function() {
+        var searchText = (editTextarea.value || "").trim().toLowerCase();
+
+        gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) {
+            var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1;
+            elem.style.display = visible ? "" : "none";
+        });
+
+        if (searchText != "") {
+            settingsShowAllTabs();
+        } else {
+            settingsShowOneTab();
+        }
+    });
+
+    settings_tabs.insertBefore(edit, settings_tabs.firstChild);
+    settings_tabs.appendChild(buttonShowAllPages);
+
+
+    buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
+});
+
+
+onOptionsChanged(function() {
+    if (gradioApp().querySelector('#settings .settings-category')) return;
+
+    var sectionMap = {};
+    gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
+        sectionMap[x.textContent.trim()] = x;
+    });
+
+    opts._categories.forEach(function(x) {
+        var section = x[0];
+        var category = x[1];
+
+        var span = document.createElement('SPAN');
+        span.textContent = category;
+        span.className = 'settings-category';
+
+        var sectionElem = sectionMap[section];
+        if (!sectionElem) return;
+
+        sectionElem.parentElement.insertBefore(span, sectionElem);
+    });
+});
+

javascript/token-counters.js

@@ -1,10 +1,9 @@
-let promptTokenCountDebounceTime = 800;
+let promptTokenCountUpdateFunctions = {};
-let promptTokenCountTimeouts = {};
-var promptTokenCountUpdateFunctions = {};
 
 function update_txt2img_tokens(...args) {
     // Called from Gradio
     update_token_counter("txt2img_token_button");
+    update_token_counter("txt2img_negative_token_button");
     if (args.length == 2) {
         return args[0];
     }
@@ -14,6 +13,7 @@ function update_txt2img_tokens(...args) {
 function update_img2img_tokens(...args) {
     // Called from Gradio
     update_token_counter("img2img_token_button");
+    update_token_counter("img2img_negative_token_button");
     if (args.length == 2) {
         return args[0];
     }
@@ -21,16 +21,7 @@ function update_img2img_tokens(...args) {
 }
 
 function update_token_counter(button_id) {
-    if (opts.disable_token_counters) {
+    promptTokenCountUpdateFunctions[button_id]?.();
-        return;
-    }
-    if (promptTokenCountTimeouts[button_id]) {
-        clearTimeout(promptTokenCountTimeouts[button_id]);
-    }
-    promptTokenCountTimeouts[button_id] = setTimeout(
-        () => gradioApp().getElementById(button_id)?.click(),
-        promptTokenCountDebounceTime,
-    );
 }
 
 
@@ -69,10 +60,11 @@ function setupTokenCounting(id, id_counter, id_button) {
     prompt.parentElement.insertBefore(counter, prompt);
     prompt.parentElement.style.position = "relative";
 
-    promptTokenCountUpdateFunctions[id] = function() {
+    var func = onEdit(id, textarea, 800, function() {
-        update_token_counter(id_button);
+        gradioApp().getElementById(id_button)?.click();
-    };
+    });
-    textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
+    promptTokenCountUpdateFunctions[id] = func;
+    promptTokenCountUpdateFunctions[id_button] = func;
 }
 
 function setupTokenCounters() {

javascript/ui.js

@@ -263,21 +263,6 @@ onAfterUiUpdate(function() {
     json_elem.parentElement.style.display = "none";
 
     setupTokenCounters();
-
-    var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
-    var settings_tabs = gradioApp().querySelector('#settings div');
-    if (show_all_pages && settings_tabs) {
-        settings_tabs.appendChild(show_all_pages);
-        show_all_pages.onclick = function() {
-            gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
-                if (elem.id == "settings_tab_licenses") {
-                    return;
-                }
-
-                elem.style.display = "block";
-            });
-        };
-    }
 });
 
 onOptionsChanged(function() {
@@ -366,3 +351,20 @@ function switchWidthHeight(tabname) {
     updateInput(height);
     return [];
 }
+
+
+var onEditTimers = {};
+
+// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
+function onEdit(editId, elem, afterMs, func) {
+    var edited = function() {
+        var existingTimer = onEditTimers[editId];
+        if (existingTimer) clearTimeout(existingTimer);
+
+        onEditTimers[editId] = setTimeout(func, afterMs);
+    };
+
+    elem.addEventListener("input", edited);
+
+    return edited;
+}

modules/api/api.py

@@ -17,19 +17,18 @@ 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, shared_items
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
-from PIL import PngImagePlugin,Image
+from PIL import PngImagePlugin, Image
-from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
-from typing import Dict, List, Any
+from typing import Any
 import piexif
 import piexif.helper
 from contextlib import closing
@@ -103,7 +102,8 @@ def decode_base64_to_image(encoding):
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
-
+        if isinstance(image, str):
+            return image
         if opts.samples_format.lower() == 'png':
             use_metadata = False
             metadata = PngImagePlugin.PngInfo()
@@ -221,15 +221,15 @@ class Api:
         self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
         self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
-        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
-        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
+        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
-        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
-        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
+        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
-        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
-        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
-        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
-        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/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"])
@@ -242,7 +242,8 @@ class Api:
         self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
-        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
+        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
+        self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
 
         if shared.cmd_opts.api_server_stop:
             self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@@ -473,9 +474,6 @@ class Api:
         return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
     def pnginfoapi(self, req: models.PNGInfoRequest):
-        if(not req.image.strip()):
-            return models.PNGInfoResponse(info="")
-
         image = decode_base64_to_image(req.image.strip())
         if image is None:
             return models.PNGInfoResponse(info="")
@@ -484,9 +482,10 @@ class Api:
         if geninfo is None:
             geninfo = ""
 
-        items = {**{'parameters': geninfo}, **items}
+        params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
+        script_callbacks.infotext_pasted_callback(geninfo, params)
 
-        return models.PNGInfoResponse(info=geninfo, items=items)
+        return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
 
     def progressapi(self, req: models.ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
@@ -541,12 +540,12 @@ class Api:
         return {}
 
     def unloadapi(self):
-        unload_model_weights()
+        sd_models.unload_model_weights()
 
         return {}
 
     def reloadapi(self):
-        reload_model_weights()
+        sd_models.send_model_to_device(shared.sd_model)
 
         return {}
 
@@ -564,9 +563,9 @@ class Api:
 
         return options
 
-    def set_config(self, req: Dict[str, Any]):
+    def set_config(self, req: dict[str, Any]):
         checkpoint_name = req.get("sd_model_checkpoint", None)
-        if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
+        if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
             raise RuntimeError(f"model {checkpoint_name!r} not found")
 
         for k, v in req.items():
@@ -770,6 +769,25 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
+    def get_extensions_list(self):
+        from modules import extensions
+        extensions.list_extensions()
+        ext_list = []
+        for ext in extensions.extensions:
+            ext: extensions.Extension
+            ext.read_info_from_repo()
+            if ext.remote is not None:
+                ext_list.append({
+                    "name": ext.name,
+                    "remote": ext.remote,
+                    "branch": ext.branch,
+                    "commit_hash":ext.commit_hash,
+                    "commit_date":ext.commit_date,
+                    "version":ext.version,
+                    "enabled":ext.enabled
+                })
+        return ext_list
+
     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, root_path=root_path)

modules/api/models.py

@@ -1,12 +1,10 @@
 import inspect
 
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional
+from typing import Any, Optional, Literal
-from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
-from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -130,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
 class ImageToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -168,17 +166,18 @@ class FileData(BaseModel):
     name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
 
 class PNGInfoRequest(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded PNG image")
 
 class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
-    items: dict = Field(title="Items", description="An object containing all the info the image had")
+    items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
+    parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
 
 class ProgressRequest(BaseModel):
     skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
@@ -232,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: List[str] = Field(title="Aliases")
+    aliases: list[str] = Field(title="Aliases")
-    options: Dict[str, str] = Field(title="Options")
+    options: dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
@@ -284,8 +283,8 @@ class EmbeddingItem(BaseModel):
     vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
 
 class EmbeddingsResponse(BaseModel):
-    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
-    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
+    skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
 
 class MemoryResponse(BaseModel):
     ram: dict = Field(title="RAM", description="System memory stats")
@@ -303,11 +302,20 @@ class ScriptArg(BaseModel):
     minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
     maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
     step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
-    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+    choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
 
 
 class ScriptInfo(BaseModel):
     name: str = Field(default=None, title="Name", description="Script name")
     is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
     is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
-    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
+    args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
+
+class ExtensionItem(BaseModel):
+    name: str = Field(title="Name", description="Extension name")
+    remote: str = Field(title="Remote", description="Extension Repository URL")
+    branch: str = Field(title="Branch", description="Extension Repository Branch")
+    commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
+    version: str = Field(title="Version", description="Extension Version")
+    commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
+    enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")

modules/cache.py

@@ -32,7 +32,7 @@ def dump_cache():
         with cache_lock:
             cache_filename_tmp = cache_filename + "-"
             with open(cache_filename_tmp, "w", encoding="utf8") as file:
-                json.dump(cache_data, file, indent=4)
+                json.dump(cache_data, file, indent=4, ensure_ascii=False)
 
             os.replace(cache_filename_tmp, cache_filename)
 

modules/cmd_args.py

@@ -90,7 +90,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
 parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
 parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
 parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False)  # Legacy compatibility, use as default value shared.opts.enable_console_prompts
 parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
@@ -107,13 +107,14 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
 parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
-parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions")
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
 parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
-parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
+parser.add_argument('--add-stop-route', action='store_true', help='does not do anything')
 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)
+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)
+parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )

modules/config_states.py

@@ -4,7 +4,6 @@ Supports saving and restoring webui and extensions from a known working set of c
 
 import os
 import json
-import time
 import tqdm
 
 from datetime import datetime
@@ -38,7 +37,7 @@ def list_config_states():
     config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
 
     for cs in config_states:
-        timestamp = time.asctime(time.gmtime(cs["created_at"]))
+        timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
         name = cs.get("name", "Config")
         full_name = f"{name}: {timestamp}"
         all_config_states[full_name] = cs

modules/devices.py

@@ -60,7 +60,8 @@ def enable_tf32():
 
         # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
         # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
-        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
+        device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
+        if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
             torch.backends.cudnn.benchmark = True
 
         torch.backends.cuda.matmul.allow_tf32 = True

modules/errors.py

@@ -6,6 +6,21 @@ import traceback
 exception_records = []
 
 
+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:
+        return list(reversed(exception_records))
+    except Exception as e:
+        return str(e)
+
+
 def record_exception():
     _, e, tb = sys.exc_info()
     if e is None:
@@ -14,8 +29,7 @@ def record_exception():
     if exception_records and exception_records[-1] == e:
         return
 
-    from modules import sysinfo
+    exception_records.append(format_exception(e, tb))
-    exception_records.append(sysinfo.format_exception(e, tb))
 
     if len(exception_records) > 5:
         exception_records.pop(0)

modules/extensions.py

@@ -1,11 +1,14 @@
+from __future__ import annotations
+
+import configparser
 import os
 import threading
+import re
 
 from modules import shared, errors, cache, scripts
 from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
-extensions = []
 
 os.makedirs(extensions_dir, exist_ok=True)
 
@@ -19,11 +22,55 @@ def active():
         return [x for x in extensions if x.enabled]
 
 
+class ExtensionMetadata:
+    filename = "metadata.ini"
+    config: configparser.ConfigParser
+    canonical_name: str
+    requires: list
+
+    def __init__(self, path, canonical_name):
+        self.config = configparser.ConfigParser()
+
+        filepath = os.path.join(path, self.filename)
+        if os.path.isfile(filepath):
+            try:
+                self.config.read(filepath)
+            except Exception:
+                errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
+
+        self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
+        self.canonical_name = canonical_name.lower().strip()
+
+        self.requires = self.get_script_requirements("Requires", "Extension")
+
+    def get_script_requirements(self, field, section, extra_section=None):
+        """reads a list of requirements from the config; field is the name of the field in the ini file,
+        like Requires or Before, and section is the name of the [section] in the ini file; additionally,
+        reads more requirements from [extra_section] if specified."""
+
+        x = self.config.get(section, field, fallback='')
+
+        if extra_section:
+            x = x + ', ' + self.config.get(extra_section, field, fallback='')
+
+        return self.parse_list(x.lower())
+
+    def parse_list(self, text):
+        """converts a line from config ("ext1 ext2, ext3  ") into a python list (["ext1", "ext2", "ext3"])"""
+
+        if not text:
+            return []
+
+        # both "," and " " are accepted as separator
+        return [x for x in re.split(r"[,\s]+", text.strip()) if x]
+
+
 class Extension:
     lock = threading.Lock()
     cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
+    metadata: ExtensionMetadata
 
-    def __init__(self, name, path, enabled=True, is_builtin=False):
+    def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None):
         self.name = name
         self.path = path
         self.enabled = enabled
@@ -36,6 +83,8 @@ class Extension:
         self.branch = None
         self.remote = None
         self.have_info_from_repo = False
+        self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower())
+        self.canonical_name = metadata.canonical_name
 
     def to_dict(self):
         return {x: getattr(self, x) for x in self.cached_fields}
@@ -56,6 +105,7 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
+
         try:
             d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
             self.from_dict(d)
@@ -136,9 +186,6 @@ class Extension:
 def list_extensions():
     extensions.clear()
 
-    if not os.path.isdir(extensions_dir):
-        return
-
     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":
@@ -148,18 +195,43 @@ def list_extensions():
     elif shared.opts.disable_all_extensions == "extra":
         print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
 
-    extension_paths = []
+    loaded_extensions = {}
-    for dirname in [extensions_dir, extensions_builtin_dir]:
+
+    # scan through extensions directory and load metadata
+    for dirname in [extensions_builtin_dir, extensions_dir]:
         if not os.path.isdir(dirname):
-            return
+            continue
 
         for extension_dirname in sorted(os.listdir(dirname)):
             path = os.path.join(dirname, extension_dirname)
             if not os.path.isdir(path):
                 continue
 
-            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+            canonical_name = extension_dirname
+            metadata = ExtensionMetadata(path, canonical_name)
 
-    for dirname, path, is_builtin in extension_paths:
+            # check for duplicated canonical names
-        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
+            already_loaded_extension = loaded_extensions.get(metadata.canonical_name)
-        extensions.append(extension)
+            if already_loaded_extension is not None:
+                errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False)
+                continue
+
+            is_builtin = dirname == extensions_builtin_dir
+            extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata)
+            extensions.append(extension)
+            loaded_extensions[canonical_name] = extension
+
+    # check for requirements
+    for extension in extensions:
+        for req in extension.metadata.requires:
+            required_extension = loaded_extensions.get(req)
+            if required_extension is None:
+                errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
+                continue
+
+            if not extension.enabled:
+                errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
+                continue
+
+
+extensions: list[Extension] = []

modules/generation_parameters_copypaste.py

@@ -9,7 +9,7 @@ from modules.paths import data_path
 from modules import shared, ui_tempdir, script_callbacks, processing
 from PIL import Image
 
-re_param_code = r'\s*([\w ]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")

modules/gfpgan_model.py

@@ -9,6 +9,7 @@ from modules import paths, shared, devices, modelloader, errors
 model_dir = "GFPGAN"
 user_path = None
 model_path = os.path.join(paths.models_path, model_dir)
+model_file_path = None
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
 have_gfpgan = False
 loaded_gfpgan_model = None
@@ -17,6 +18,7 @@ loaded_gfpgan_model = None
 def gfpgann():
     global loaded_gfpgan_model
     global model_path
+    global model_file_path
     if loaded_gfpgan_model is not None:
         loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
         return loaded_gfpgan_model
@@ -24,17 +26,24 @@ def gfpgann():
     if gfpgan_constructor is None:
         return None
 
-    models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+    models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
+
     if len(models) == 1 and models[0].startswith("http"):
         model_file = models[0]
     elif len(models) != 0:
-        latest_file = max(models, key=os.path.getctime)
+        gfp_models = []
+        for item in models:
+            if 'GFPGAN' in os.path.basename(item):
+                gfp_models.append(item)
+        latest_file = max(gfp_models, key=os.path.getctime)
         model_file = latest_file
     else:
         print("Unable to load gfpgan model!")
         return None
+
     if hasattr(facexlib.detection.retinaface, 'device'):
         facexlib.detection.retinaface.device = devices.device_gfpgan
+    model_file_path = model_file
     model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
@@ -77,19 +86,25 @@ def setup_model(dirname):
         global user_path
         global have_gfpgan
         global gfpgan_constructor
+        global model_file_path
+
+        facexlib_path = model_path
+
+        if dirname is not None:
+            facexlib_path = dirname
 
         load_file_from_url_orig = gfpgan.utils.load_file_from_url
         facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
         facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
 
         def my_load_file_from_url(**kwargs):
-            return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
+            return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))
 
         def facex_load_file_from_url(**kwargs):
-            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
+            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
 
         def facex_load_file_from_url2(**kwargs):
-            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
+            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
 
         gfpgan.utils.load_file_from_url = my_load_file_from_url
         facexlib.detection.load_file_from_url = facex_load_file_from_url

modules/gitpython_hack.py

@@ -23,7 +23,7 @@ class Git(git.Git):
         )
         return self._parse_object_header(ret)
 
-    def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
+    def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
         # Not really streaming, per se; this buffers the entire object in memory.
         # Shouldn't be a problem for our use case, since we're only using this for
         # object headers (commit objects).

modules/hypernetworks/hypernetwork.py

@@ -468,7 +468,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
     shared.reload_hypernetworks()
 
 
-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):
+def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int):
     from modules import images, processing
 
     save_hypernetwork_every = save_hypernetwork_every or 0
@@ -698,7 +698,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                         p.prompt = preview_prompt
                         p.negative_prompt = preview_negative_prompt
                         p.steps = preview_steps
-                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
                         p.cfg_scale = preview_cfg_scale
                         p.seed = preview_seed
                         p.width = preview_width

modules/images.py

@@ -561,6 +561,8 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
             })
 
             piexif.insert(exif_bytes, filename)
+    elif extension.lower() == ".gif":
+        image.save(filename, format=image_format, comment=geninfo)
     else:
         image.save(filename, format=image_format, quality=opts.jpeg_quality)
 
@@ -661,7 +663,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
         save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
 
-        os.replace(temp_file_path, filename_without_extension + extension)
+        filename = filename_without_extension + extension
+        if shared.opts.save_images_replace_action != "Replace":
+            n = 0
+            while os.path.exists(filename):
+                n += 1
+                filename = f"{filename_without_extension}-{n}{extension}"
+        os.replace(temp_file_path, filename)
 
     fullfn_without_extension, extension = os.path.splitext(params.filename)
     if hasattr(os, 'statvfs'):
@@ -718,7 +726,12 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
     geninfo = items.pop('parameters', None)
 
     if "exif" in items:
-        exif = piexif.load(items["exif"])
+        exif_data = items["exif"]
+        try:
+            exif = piexif.load(exif_data)
+        except OSError:
+            # memory / exif was not valid so piexif tried to read from a file
+            exif = None
         exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
         try:
             exif_comment = piexif.helper.UserComment.load(exif_comment)
@@ -728,6 +741,8 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
         if exif_comment:
             items['exif comment'] = exif_comment
             geninfo = exif_comment
+    elif "comment" in items: # for gif
+        geninfo = items["comment"].decode('utf8', errors="ignore")
 
     for field in IGNORED_INFO_KEYS:
         items.pop(field, None)

modules/img2img.py

@@ -10,6 +10,7 @@ from modules import 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.sd_models import get_closet_checkpoint_match
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
@@ -41,7 +42,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
     cfg_scale = p.cfg_scale
     sampler_name = p.sampler_name
     steps = p.steps
-
+    override_settings = p.override_settings
+    sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
+    batch_results = None
+    discard_further_results = False
     for i, image in enumerate(images):
         state.job = f"{i+1} out of {len(images)}"
         if state.skipped:
@@ -104,16 +108,42 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
             p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
             p.steps = int(parsed_parameters.get("Steps", steps))
 
+            model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
+            if model_info is not None:
+                p.override_settings['sd_model_checkpoint'] = model_info.name
+            elif sd_model_checkpoint_override:
+                p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
+            else:
+                p.override_settings.pop("sd_model_checkpoint", None)
+
+        if output_dir:
+            p.outpath_samples = output_dir
+            p.override_settings['save_to_dirs'] = False
+            p.override_settings['save_images_replace_action'] = "Add number suffix"
+            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}'
+
         proc = modules.scripts.scripts_img2img.run(p, *args)
+
         if proc is None:
-            if output_dir:
+            p.override_settings.pop('save_images_replace_action', None)
-                p.outpath_samples = output_dir
+            proc = process_images(p)
-                p.override_settings['save_to_dirs'] = False
+
-                if p.n_iter > 1 or p.batch_size > 1:
+        if not discard_further_results and proc:
-                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+            if batch_results:
-                else:
+                batch_results.images.extend(proc.images)
-                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+                batch_results.infotexts.extend(proc.infotexts)
-            process_images(p)
+            else:
+                batch_results = proc
+
+            if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
+                discard_further_results = True
+                batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
+                batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
+
+    return batch_results
 
 
 def img2img(id_task: str,
@@ -232,7 +262,7 @@ def img2img(id_task: str,
 
     p.user = request.username
 
-    if shared.cmd_opts.enable_console_prompts:
+    if shared.opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
     if mask:
@@ -244,10 +274,10 @@ def img2img(id_task: str,
     with closing(p):
         if is_batch:
             assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+            processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
 
-            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+            if processed is None:
-
+                processed = Processed(p, [], p.seed, "")
-            processed = Processed(p, [], p.seed, "")
         else:
             processed = modules.scripts.scripts_img2img.run(p, *args)
             if processed is None:

modules/initialize.py

@@ -151,8 +151,8 @@ def initialize_rest(*, reload_script_modules=False):
 
         from modules import devices
         devices.first_time_calculation()
-
+    if not shared.cmd_opts.skip_load_model_at_start:
-    Thread(target=load_model).start()
+        Thread(target=load_model).start()
 
     from modules import shared_items
     shared_items.reload_hypernetworks()

modules/initialize_util.py

@@ -150,10 +150,14 @@ def dumpstacks():
 
 def configure_sigint_handler():
     # make the program just exit at ctrl+c without waiting for anything
+
+    from modules import shared
+
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
 
-        dumpstacks()
+        if shared.opts.dump_stacks_on_signal:
+            dumpstacks()
 
         os._exit(0)
 

modules/launch_utils.py

@@ -64,7 +64,7 @@ Use --skip-python-version-check to suppress this warning.
 @lru_cache()
 def commit_hash():
     try:
-        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "-C", script_path, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
     except Exception:
         return "<none>"
 
@@ -72,7 +72,7 @@ def commit_hash():
 @lru_cache()
 def git_tag():
     try:
-        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "-C", script_path, "describe", "--tags"], shell=False, encoding='utf8').strip()
     except Exception:
         try:
 
@@ -441,7 +441,7 @@ def dump_sysinfo():
     import datetime
 
     text = sysinfo.get()
-    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
 
     with open(filename, "w", encoding="utf8") as file:
         file.write(text)

modules/localization.py

@@ -14,21 +14,24 @@ def list_localizations(dirname):
         if ext.lower() != ".json":
             continue
 
-        localizations[fn] = os.path.join(dirname, file)
+        localizations[fn] = [os.path.join(dirname, file)]
 
     for file in scripts.list_scripts("localizations", ".json"):
         fn, ext = os.path.splitext(file.filename)
-        localizations[fn] = file.path
+        if fn not in localizations:
+            localizations[fn] = []
+        localizations[fn].append(file.path)
 
 
 def localization_js(current_localization_name: str) -> str:
-    fn = localizations.get(current_localization_name, None)
+    fns = localizations.get(current_localization_name, None)
     data = {}
-    if fn is not None:
+    if fns is not None:
-        try:
+        for fn in fns:
-            with open(fn, "r", encoding="utf8") as file:
+            try:
-                data = json.load(file)
+                with open(fn, "r", encoding="utf8") as file:
-        except Exception:
+                    data.update(json.load(file))
-            errors.report(f"Error loading localization from {fn}", exc_info=True)
+            except Exception:
+                errors.report(f"Error loading localization from {fn}", exc_info=True)
 
     return f"window.localization = {json.dumps(data)}"

modules/logging_config.py

@@ -1,16 +1,41 @@
 import os
 import logging
 
+try:
+    from tqdm.auto import tqdm
+
+    class TqdmLoggingHandler(logging.Handler):
+        def __init__(self, level=logging.INFO):
+            super().__init__(level)
+
+        def emit(self, record):
+            try:
+                msg = self.format(record)
+                tqdm.write(msg)
+                self.flush()
+            except Exception:
+                self.handleError(record)
+
+    TQDM_IMPORTED = True
+except ImportError:
+    # tqdm does not exist before first launch
+    # I will import once the UI finishes seting up the enviroment and reloads.
+    TQDM_IMPORTED = False
 
 def setup_logging(loglevel):
     if loglevel is None:
         loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
 
+    loghandlers = []
+
+    if TQDM_IMPORTED:
+        loghandlers.append(TqdmLoggingHandler())
+
     if loglevel:
         log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
         logging.basicConfig(
             level=log_level,
             format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
             datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=loghandlers
         )
-

modules/options.py

@@ -1,5 +1,6 @@
 import json
 import sys
+from dataclasses import dataclass
 
 import gradio as gr
 
@@ -8,13 +9,14 @@ from modules.shared_cmd_options import cmd_opts
 
 
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
         self.default = default
         self.label = label
         self.component = component
         self.component_args = component_args
         self.onchange = onchange
         self.section = section
+        self.category_id = category_id
         self.refresh = refresh
         self.do_not_save = False
 
@@ -63,7 +65,11 @@ class OptionHTML(OptionInfo):
 
 def options_section(section_identifier, options_dict):
     for v in options_dict.values():
-        v.section = section_identifier
+        if len(section_identifier) == 2:
+            v.section = section_identifier
+        elif len(section_identifier) == 3:
+            v.section = section_identifier[0:2]
+            v.category_id = section_identifier[2]
 
     return options_dict
 
@@ -76,7 +82,7 @@ class Options:
 
     def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
         self.data_labels = data_labels
-        self.data = {k: v.default for k, v in self.data_labels.items()}
+        self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
         self.restricted_opts = restricted_opts
 
     def __setattr__(self, key, value):
@@ -158,7 +164,7 @@ class Options:
         assert not cmd_opts.freeze_settings, "saving settings is disabled"
 
         with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
+            json.dump(self.data, file, indent=4, ensure_ascii=False)
 
     def same_type(self, x, y):
         if x is None or y is None:
@@ -206,21 +212,59 @@ class Options:
         d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
         d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
         d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
+
+        item_categories = {}
+        for item in self.data_labels.values():
+            category = categories.mapping.get(item.category_id)
+            category = "Uncategorized" if category is None else category.label
+            if category not in item_categories:
+                item_categories[category] = item.section[1]
+
+        # _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
+        d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
+
         return json.dumps(d)
 
     def add_option(self, key, info):
         self.data_labels[key] = info
+        if key not in self.data and not info.do_not_save:
+            self.data[key] = info.default
 
     def reorder(self):
-        """reorder settings so that all items related to section always go together"""
+        """Reorder settings so that:
+            - all items related to section always go together
+            - all sections belonging to a category go together
+            - sections inside a category are ordered alphabetically
+            - categories are ordered by creation order
+
+        Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
+
+        This function also changes items' category_id so that all items belonging to a section have the same category_id.
+        """
+
+        category_ids = {}
+        section_categories = {}
 
-        section_ids = {}
         settings_items = self.data_labels.items()
         for _, item in settings_items:
-            if item.section not in section_ids:
+            if item.section not in section_categories:
-                section_ids[item.section] = len(section_ids)
+                section_categories[item.section] = item.category_id
 
-        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+        for _, item in settings_items:
+            item.category_id = section_categories.get(item.section)
+
+        for category_id in categories.mapping:
+            if category_id not in category_ids:
+                category_ids[category_id] = len(category_ids)
+
+        def sort_key(x):
+            item: OptionInfo = x[1]
+            category_order = category_ids.get(item.category_id, len(category_ids))
+            section_order = item.section[1]
+
+            return category_order, section_order
+
+        self.data_labels = dict(sorted(settings_items, key=sort_key))
 
     def cast_value(self, key, value):
         """casts an arbitrary to the same type as this setting's value with key
@@ -243,3 +287,22 @@ class Options:
             value = expected_type(value)
 
         return value
+
+
+@dataclass
+class OptionsCategory:
+    id: str
+    label: str
+
+class OptionsCategories:
+    def __init__(self):
+        self.mapping = {}
+
+    def register_category(self, category_id, label):
+        if category_id in self.mapping:
+            return category_id
+
+        self.mapping[category_id] = OptionsCategory(category_id, label)
+
+
+categories = OptionsCategories()

modules/paths.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir  # noqa: F401
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, cwd  # noqa: F401
 
 import modules.safe  # noqa: F401
 

modules/paths_internal.py

@@ -8,6 +8,7 @@ import shlex
 commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 sys.argv += shlex.split(commandline_args)
 
+cwd = os.getcwd()
 modules_path = os.path.dirname(os.path.realpath(__file__))
 script_path = os.path.dirname(modules_path)
 

modules/postprocessing.py

@@ -78,7 +78,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
         image_data.close()
 
     devices.torch_gc()
-
+    shared.state.end()
     return outputs, ui_common.plaintext_to_html(infotext), ''
 
 

modules/processing.py

@@ -149,7 +149,7 @@ class StableDiffusionProcessing:
     masks_for_overlay: list = None
     eta: float = None
     do_not_reload_embeddings: bool = False
-    denoising_strength: float = 0
+    denoising_strength: float = None
     ddim_discretize: str = None
     s_min_uncond: float = None
     s_churn: float = None
@@ -303,7 +303,7 @@ class StableDiffusionProcessing:
         return conditioning
 
     def edit_image_conditioning(self, source_image):
-        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
 
         return conditioning_image
 
@@ -537,6 +537,7 @@ class Processed:
         self.all_seeds = all_seeds or p.all_seeds or [self.seed]
         self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
         self.infotexts = infotexts or [info]
+        self.version = program_version()
 
     def js(self):
         obj = {
@@ -571,6 +572,7 @@ class Processed:
             "job_timestamp": self.job_timestamp,
             "clip_skip": self.clip_skip,
             "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+            "version": self.version,
         }
 
         return json.dumps(obj)
@@ -713,7 +715,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     if p.scripts is not None:
         p.scripts.before_process(p)
 
-    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+    stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
 
     try:
         # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@@ -801,7 +803,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     infotexts = []
     output_images = []
-
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -876,7 +877,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             else:
                 if opts.sd_vae_decode_method != 'Full':
                     p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
-
                 # Generate the mask(s) based on similarity between the original and denoised latent vectors
                 if getattr(p, "image_mask", None) is not None:
                     # latent_mask = p.nmask[0].float().cpu()
@@ -943,6 +943,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             devices.torch_gc()
 
+            state.nextjob()
+
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
@@ -1025,7 +1027,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             devices.torch_gc()
 
-            state.nextjob()
+        if not infotexts:
+            infotexts.append(Processed(p, []).infotext(p, 0))
 
         p.color_corrections = None
 
@@ -1211,6 +1214,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         if not self.enable_hr:
             return samples
+        devices.torch_gc()
 
         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)
@@ -1220,8 +1224,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         with sd_models.SkipWritingToConfig():
             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)
 
     def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@@ -1229,7 +1231,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             return samples
 
         self.is_hr_pass = True
-
         target_width = self.hr_upscale_to_x
         target_height = self.hr_upscale_to_y
 
@@ -1318,7 +1319,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
 
         self.is_hr_pass = False
-
         return decoded_samples
 
     def close(self):

modules/processing_scripts/seed.py

@@ -29,8 +29,8 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
             else:
                 self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
 
-            random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
+            random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), tooltip="Set seed to -1, which will cause a new random number to be used every time")
-            reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
+            reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), tooltip="Reuse seed from last generation, mostly useful if it was randomized")
 
             seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
 

modules/prompt_parser.py

@@ -2,10 +2,9 @@ from __future__ import annotations
 
 import re
 from collections import namedtuple
-from typing import List
 import lark
 
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
 # [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: List[ScheduledPromptConditioning] = schedules
+        self.schedules: list[ScheduledPromptConditioning] = schedules
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
 
 
 def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
@@ -278,7 +277,7 @@ class DictWithShape(dict):
         return self["crossattn"].shape
 
 
-def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
     is_dict = isinstance(param, dict)
 

modules/restart.py

@@ -14,7 +14,9 @@ def is_restartable() -> bool:
 def restart_program() -> None:
     """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
 
-    (Path(script_path) / "tmp" / "restart").touch()
+    tmpdir = Path(script_path) / "tmp"
+    tmpdir.mkdir(parents=True, exist_ok=True)
+    (tmpdir / "restart").touch()
 
     stop_program()
 

modules/rng.py

@@ -110,7 +110,7 @@ class ImageRNG:
         self.is_first = True
 
     def first(self):
-        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
+        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
 
         xs = []
 

modules/script_callbacks.py

@@ -1,7 +1,7 @@
 import inspect
 import os
 from collections import namedtuple
-from typing import Optional, Dict, Any
+from typing import Optional, Any
 
 from fastapi import FastAPI
 from gradio import Blocks
@@ -258,7 +258,7 @@ def image_grid_callback(params: ImageGridLoopParams):
             report_exception(c, 'image_grid')
 
 
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
+def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
     for c in callback_map['callbacks_infotext_pasted']:
         try:
             c.callback(infotext, params)
@@ -449,7 +449,7 @@ def on_infotext_pasted(callback):
     """register a function to be called before applying an infotext.
     The callback is called with two arguments:
        - infotext: str - raw infotext.
-       - result: Dict[str, any] - parsed infotext parameters.
+       - result: dict[str, any] - parsed infotext parameters.
     """
     add_callback(callback_map['callbacks_infotext_pasted'], callback)
 

modules/scripts.py

@@ -311,20 +311,113 @@ scripts_data = []
 postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 
+def topological_sort(dependencies):
+    """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
+    Ignores errors relating to missing dependeencies or circular dependencies
+    """
+
+    visited = {}
+    result = []
+
+    def inner(name):
+        visited[name] = True
+
+        for dep in dependencies.get(name, []):
+            if dep in dependencies and dep not in visited:
+                inner(dep)
+
+        result.append(name)
+
+    for depname in dependencies:
+        if depname not in visited:
+            inner(depname)
+
+    return result
+
+
+@dataclass
+class ScriptWithDependencies:
+    script_canonical_name: str
+    file: ScriptFile
+    requires: list
+    load_before: list
+    load_after: list
+
 
 def list_scripts(scriptdirname, extension, *, include_extensions=True):
-    scripts_list = []
+    scripts = {}
 
-    basedir = os.path.join(paths.script_path, scriptdirname)
+    loaded_extensions = {ext.canonical_name: ext for ext in extensions.active()}
-    if os.path.exists(basedir):
+    loaded_extensions_scripts = {ext.canonical_name: [] for ext in extensions.active()}
-        for filename in sorted(os.listdir(basedir)):
+
-            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
+    # build script dependency map
+    root_script_basedir = os.path.join(paths.script_path, scriptdirname)
+    if os.path.exists(root_script_basedir):
+        for filename in sorted(os.listdir(root_script_basedir)):
+            if not os.path.isfile(os.path.join(root_script_basedir, filename)):
+                continue
+
+            if os.path.splitext(filename)[1].lower() != extension:
+                continue
+
+            script_file = ScriptFile(paths.script_path, filename, os.path.join(root_script_basedir, filename))
+            scripts[filename] = ScriptWithDependencies(filename, script_file, [], [], [])
 
     if include_extensions:
         for ext in extensions.active():
-            scripts_list += ext.list_files(scriptdirname, extension)
+            extension_scripts_list = ext.list_files(scriptdirname, extension)
+            for extension_script in extension_scripts_list:
+                if not os.path.isfile(extension_script.path):
+                    continue
 
-    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+                script_canonical_name = ("builtin/" if ext.is_builtin else "") + ext.canonical_name + "/" + extension_script.filename
+                relative_path = scriptdirname + "/" + extension_script.filename
+
+                script = ScriptWithDependencies(
+                    script_canonical_name=script_canonical_name,
+                    file=extension_script,
+                    requires=ext.metadata.get_script_requirements("Requires", relative_path, scriptdirname),
+                    load_before=ext.metadata.get_script_requirements("Before", relative_path, scriptdirname),
+                    load_after=ext.metadata.get_script_requirements("After", relative_path, scriptdirname),
+                )
+
+                scripts[script_canonical_name] = script
+                loaded_extensions_scripts[ext.canonical_name].append(script)
+
+    for script_canonical_name, script in scripts.items():
+        # load before requires inverse dependency
+        # in this case, append the script name into the load_after list of the specified script
+        for load_before in script.load_before:
+            # if this requires an individual script to be loaded before
+            other_script = scripts.get(load_before)
+            if other_script:
+                other_script.load_after.append(script_canonical_name)
+
+            # if this requires an extension
+            other_extension_scripts = loaded_extensions_scripts.get(load_before)
+            if other_extension_scripts:
+                for other_script in other_extension_scripts:
+                    other_script.load_after.append(script_canonical_name)
+
+        # if After mentions an extension, remove it and instead add all of its scripts
+        for load_after in list(script.load_after):
+            if load_after not in scripts and load_after in loaded_extensions_scripts:
+                script.load_after.remove(load_after)
+
+                for other_script in loaded_extensions_scripts.get(load_after, []):
+                    script.load_after.append(other_script.script_canonical_name)
+
+    dependencies = {}
+
+    for script_canonical_name, script in scripts.items():
+        for required_script in script.requires:
+            if required_script not in scripts and required_script not in loaded_extensions:
+                errors.report(f'Script "{script_canonical_name}" requires "{required_script}" to be loaded, but it is not.', exc_info=False)
+
+        dependencies[script_canonical_name] = script.load_after
+
+    ordered_scripts = topological_sort(dependencies)
+    scripts_list = [scripts[script_canonical_name].file for script_canonical_name in ordered_scripts]
 
     return scripts_list
 
@@ -365,15 +458,9 @@ def load_scripts():
             elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                 postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
 
-    def orderby(basedir):
+    # here the scripts_list is already ordered
-        # 1st webui, 2nd extensions-builtin, 3rd extensions
+    # processing_script is not considered though
-        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+    for scriptfile in scripts_list:
-        for key in priority:
-            if basedir.startswith(key):
-                return priority[key]
-        return 9999
-
-    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
         try:
             if scriptfile.basedir != paths.script_path:
                 sys.path = [scriptfile.basedir] + sys.path
@@ -491,11 +578,15 @@ class ScriptRunner:
 
             arg_info = api_models.ScriptArg(label=control.label or "")
 
-            for field in ("value", "minimum", "maximum", "step", "choices"):
+            for field in ("value", "minimum", "maximum", "step"):
                 v = getattr(control, field, None)
                 if v is not None:
                     setattr(arg_info, field, v)
 
+            choices = getattr(control, 'choices', None)  # as of gradio 3.41, some items in choices are strings, and some are tuples where the first elem is the string
+            if choices is not None:
+                arg_info.choices = [x[0] if isinstance(x, tuple) else x for x in choices]
+
             api_args.append(arg_info)
 
         script.api_info = api_models.ScriptInfo(

modules/sd_hijack.py

@@ -2,14 +2,15 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType
 
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
 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, xlmr_m18
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
+import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
@@ -37,6 +38,8 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
 
+ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
 
 def list_optimizers():
     new_optimizers = script_callbacks.list_optimizers_callback()
@@ -181,6 +184,20 @@ class StableDiffusionModelHijack:
             errors.display(e, "applying cross attention optimization")
             undo_optimizations()
 
+    def convert_sdxl_to_ssd(self, m):
+        """Converts an SDXL model to a Segmind Stable Diffusion model (see https://huggingface.co/segmind/SSD-1B)"""
+
+        delattr(m.model.diffusion_model.middle_block, '1')
+        delattr(m.model.diffusion_model.middle_block, '2')
+        for i in ['9', '8', '7', '6', '5', '4']:
+            delattr(m.model.diffusion_model.input_blocks[7][1].transformer_blocks, i)
+            delattr(m.model.diffusion_model.input_blocks[8][1].transformer_blocks, i)
+            delattr(m.model.diffusion_model.output_blocks[0][1].transformer_blocks, i)
+            delattr(m.model.diffusion_model.output_blocks[1][1].transformer_blocks, i)
+        delattr(m.model.diffusion_model.output_blocks[4][1].transformer_blocks, '1')
+        delattr(m.model.diffusion_model.output_blocks[5][1].transformer_blocks, '1')
+        devices.torch_gc()
+
     def hijack(self, m):
         conditioner = getattr(m, 'conditioner', None)
         if conditioner:
@@ -208,7 +225,7 @@ class StableDiffusionModelHijack:
             else:
                 m.cond_stage_model = conditioner
 
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
             m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
@@ -239,10 +256,17 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
-        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+        import modules.models.diffusion.ddpm_edit
-            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+        if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
+            sd_unet.original_forward = ldm_original_forward
+        elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
+            sd_unet.original_forward = ldm_original_forward
+        elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
+            sd_unet.original_forward = sgm_original_forward
+        else:
+            sd_unet.original_forward = None
 
-        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
         conditioner = getattr(m, 'conditioner', None)
@@ -279,7 +303,8 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
-        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+        sd_unet.original_forward = None
+
 
     def apply_circular(self, enable):
         if self.circular_enabled == enable:

modules/sd_models.py

@@ -1,22 +1,22 @@
 import collections
 import os.path
 import sys
-import gc
 import threading
 
 import torch
 import re
 import safetensors.torch
-from omegaconf import OmegaConf
+from omegaconf import OmegaConf, ListConfig
 from os import mkdir
 from urllib import request
 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, sd_models_xl, cache, extra_networks, processing, lowvram, sd_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, extra_networks, processing, lowvram, sd_hijack, patches
 from modules.timer import Timer
 import tomesd
+import numpy as np
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -49,11 +49,12 @@ class CheckpointInfo:
     def __init__(self, filename):
         self.filename = filename
         abspath = os.path.abspath(filename)
+        abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
 
         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):
+        if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
-            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
+            name = abspath.replace(abs_ckpt_dir, '')
         elif abspath.startswith(model_path):
             name = abspath.replace(model_path, '')
         else:
@@ -129,9 +130,12 @@ except Exception:
 
 
 def setup_model():
+    """called once at startup to do various one-time tasks related to SD models"""
+
     os.makedirs(model_path, exist_ok=True)
 
     enable_midas_autodownload()
+    patch_given_betas()
 
 
 def checkpoint_tiles(use_short=False):
@@ -309,6 +313,8 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
     if checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
         print(f"Loading weights [{sd_model_hash}] from cache")
+        # move to end as latest
+        checkpoints_loaded.move_to_end(checkpoint_info)
         return checkpoints_loaded[checkpoint_info]
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@@ -346,16 +352,19 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, 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
-
+    model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
     if model.is_sdxl:
         sd_models_xl.extend_sdxl(model)
 
-    model.load_state_dict(state_dict, strict=False)
+    if model.is_ssd:
-    timer.record("apply weights to model")
+        sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
 
     if shared.opts.sd_checkpoint_cache > 0:
         # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = state_dict
+        checkpoints_loaded[checkpoint_info] = state_dict.copy()
+
+    model.load_state_dict(state_dict, strict=False)
+    timer.record("apply weights to model")
 
     del state_dict
 
@@ -453,6 +462,20 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
+def patch_given_betas():
+    import ldm.models.diffusion.ddpm
+
+    def patched_register_schedule(*args, **kwargs):
+        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
+
+        if isinstance(args[1], ListConfig):
+            args = (args[0], np.array(args[1]), *args[2:])
+
+        original_register_schedule(*args, **kwargs)
+
+    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
+
+
 def repair_config(sd_config):
 
     if not hasattr(sd_config.model.params, "use_ema"):
@@ -777,17 +800,7 @@ def reload_model_weights(sd_model=None, info=None):
 
 
 def unload_model_weights(sd_model=None, info=None):
-    timer = Timer()
+    send_model_to_cpu(sd_model or shared.sd_model)
-
-    if model_data.sd_model:
-        model_data.sd_model.to(devices.cpu)
-        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
-        model_data.sd_model = None
-        sd_model = None
-        gc.collect()
-        devices.torch_gc()
-
-    print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model
 

modules/sd_models_config.py

@@ -21,7 +21,7 @@ config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inf
 config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
 config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
 config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
-
+config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
 
 def is_using_v_parameterization_for_sd2(state_dict):
     """
@@ -95,7 +95,10 @@ def guess_model_config_from_state_dict(sd, filename):
         if diffusion_model_input.shape[1] == 8:
             return config_instruct_pix2pix
 
+
     if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+        if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
+            return config_alt_diffusion_m18
         return config_alt_diffusion
 
     return config_default

modules/sd_models_types.py

@@ -22,7 +22,10 @@ class WebuiSdModel(LatentDiffusion):
     """structure with additional information about the file with model's weights"""
 
     is_sdxl: bool
-    """True if the model's architecture is SDXL"""
+    """True if the model's architecture is SDXL or SSD"""
+
+    is_ssd: bool
+    """True if the model is SSD"""
 
     is_sd2: bool
     """True if the model's architecture is SD 2.x"""

modules/sd_samplers_extra.py

@@ -60,7 +60,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                 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:])])
+                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
 
     last_sigma = None
     for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):

modules/sd_unet.py

@@ -1,11 +1,11 @@
 import torch.nn
-import ldm.modules.diffusionmodules.openaimodel
 
 from modules import script_callbacks, shared, devices
 
 unet_options = []
 current_unet_option = None
 current_unet = None
+original_forward = None
 
 
 def list_unets():
@@ -88,5 +88,5 @@ def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
     if current_unet is not None:
         return current_unet.forward(x, timesteps, context, *args, **kwargs)
 
-    return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+    return original_forward(self, x, timesteps, context, *args, **kwargs)
 

modules/shared_cmd_options.py

@@ -14,5 +14,5 @@ if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
 else:
     cmd_opts, _ = parser.parse_known_args()
 
-
+cmd_opts.webui_is_non_local = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name])
-cmd_opts.disable_extension_access = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name]) and not cmd_opts.enable_insecure_extension_access
+cmd_opts.disable_extension_access = cmd_opts.webui_is_non_local and not cmd_opts.enable_insecure_extension_access

modules/shared_items.py

@@ -44,9 +44,9 @@ def refresh_unet_list():
     modules.sd_unet.list_unets()
 
 
-def list_checkpoint_tiles():
+def list_checkpoint_tiles(use_short=False):
     import modules.sd_models
-    return modules.sd_models.checkpoint_tiles()
+    return modules.sd_models.checkpoint_tiles(use_short)
 
 
 def refresh_checkpoints():
@@ -67,6 +67,8 @@ def reload_hypernetworks():
 
 
 ui_reorder_categories_builtin_items = [
+    "prompt",
+    "image",
     "inpaint",
     "sampler",
     "accordions",

modules/shared_options.py

@@ -3,7 +3,7 @@ import gradio as gr
 from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
 from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir  # noqa: F401
 from modules.shared_cmd_options import cmd_opts
-from modules.options import options_section, OptionInfo, OptionHTML
+from modules.options import options_section, OptionInfo, OptionHTML, categories
 
 options_templates = {}
 hide_dirs = shared.hide_dirs
@@ -21,12 +21,19 @@ restricted_opts = {
     "outdir_init_images"
 }
 
-options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+categories.register_category("saving", "Saving images")
+categories.register_category("sd", "Stable Diffusion")
+categories.register_category("ui", "User Interface")
+categories.register_category("system", "System")
+categories.register_category("postprocessing", "Postprocessing")
+categories.register_category("training", "Training")
+
+options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
     "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
     "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
-
+    "save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
     "grid_format": OptionInfo('png', 'File format for grids'),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
@@ -62,9 +69,12 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
 
     "save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
+
+    "notification_audio": OptionInfo(True, "Play notification sound after image generation").info("notification.mp3 should be present in the root directory").needs_reload_ui(),
+    "notification_volume": OptionInfo(100, "Notification sound volume", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}).info("in %"),
 }))
 
-options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+options_templates.update(options_section(('saving-paths', "Paths for saving", "saving"), {
     "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
     "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
     "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
@@ -76,7 +86,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
     "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))
 
-options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory", "saving"), {
     "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
@@ -84,22 +94,23 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
-options_templates.update(options_section(('upscaling', "Upscaling"), {
+options_templates.update(options_section(('upscaling', "Upscaling", "postprocessing"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
 }))
 
-options_templates.update(options_section(('face-restoration', "Face restoration"), {
+options_templates.update(options_section(('face-restoration', "Face restoration", "postprocessing"), {
     "face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
     "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
 
-options_templates.update(options_section(('system', "System"), {
+options_templates.update(options_section(('system', "System", "system"), {
     "auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
+    "enable_console_prompts": OptionInfo(shared.cmd_opts.enable_console_prompts, "Print prompts to console when generating with txt2img and img2img."),
     "show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
     "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
     "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
@@ -109,15 +120,16 @@ options_templates.update(options_section(('system', "System"), {
     "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."),
+    "dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
 }))
 
-options_templates.update(options_section(('API', "API"), {
+options_templates.update(options_section(('API', "API", "system"), {
     "api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
     "api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
     "api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
 }))
 
-options_templates.update(options_section(('training', "Training"), {
+options_templates.update(options_section(('training', "Training", "training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
@@ -132,8 +144,8 @@ options_templates.update(options_section(('training', "Training"), {
     "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
 }))
 
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
+options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
-    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short)}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
     "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"),
@@ -149,14 +161,14 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
 }))
 
-options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"), {
     "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(('vae', "VAE"), {
+options_templates.update(options_section(('vae', "VAE", "sd"), {
     "sd_vae_explanation": OptionHTML("""
 <abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
 image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
@@ -171,7 +183,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
     "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
 }))
 
-options_templates.update(options_section(('img2img', "img2img"), {
+options_templates.update(options_section(('img2img', "img2img", "sd"), {
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
     "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
     "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
@@ -184,9 +196,10 @@ options_templates.update(options_section(('img2img', "img2img"), {
     "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_reload_ui(),
     "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"),
+    "img2img_batch_show_results_limit": OptionInfo(32, "Show the first N batch img2img results in UI", gr.Slider, {"minimum": -1, "maximum": 1000, "step": 1}).info('0: disable, -1: show all images. Too many images can cause lag'),
 }))
 
-options_templates.update(options_section(('optimizations', "Optimizations"), {
+options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
     "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": 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}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
@@ -197,7 +210,7 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
     "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
 }))
 
-options_templates.update(options_section(('compatibility', "Compatibility"), {
+options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
     "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@@ -222,7 +235,7 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
     "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
 }))
 
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+options_templates.update(options_section(('extra_networks', "Extra Networks", "sd"), {
     "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
     "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
     "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
@@ -230,6 +243,8 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
     "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
     "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
+    "extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
+    "extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
     "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
     "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@@ -237,7 +252,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
 }))
 
-options_templates.update(options_section(('ui', "User interface"), {
+options_templates.update(options_section(('ui', "User interface", "ui"), {
     "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
     "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
     "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
@@ -255,19 +270,24 @@ options_templates.update(options_section(('ui', "User interface"), {
     "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
-    "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
+    "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Ctrl+up/down word delimiters"),
+    "keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
     "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
     "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
     "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
     "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
     "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_reload_ui(),
+    "sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
     "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
     "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
     "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
+    "txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
+    "img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
+    "compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
 }))
 
 
-options_templates.update(options_section(('infotext', "Infotext"), {
+options_templates.update(options_section(('infotext', "Infotext", "ui"), {
     "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"),
     "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
@@ -282,7 +302,7 @@ options_templates.update(options_section(('infotext', "Infotext"), {
 
 }))
 
-options_templates.update(options_section(('ui', "Live previews"), {
+options_templates.update(options_section(('ui', "Live previews", "ui"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
     "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
     "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
@@ -295,7 +315,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
     "live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
 }))
 
-options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {
     "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
     "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
     "eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
@@ -305,8 +325,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_tmax':  OptionInfo(0.0, "sigma tmax",  gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
     'k_sched_type':  OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
-    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule min sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
-    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
+    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule max sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
     'rho':  OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
     'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", infotext='Discard penultimate sigma').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
@@ -317,7 +337,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
 }))
 
-options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
     'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
     'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
@@ -329,4 +349,3 @@ options_templates.update(options_section((None, "Hidden options"), {
     "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
     "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))
-

modules/shared_state.py

@@ -103,6 +103,7 @@ class State:
 
     def begin(self, job: str = "(unknown)"):
         self.sampling_step = 0
+        self.time_start = time.time()
         self.job_count = -1
         self.processing_has_refined_job_count = False
         self.job_no = 0
@@ -114,7 +115,6 @@ class State:
         self.skipped = False
         self.interrupted = False
         self.textinfo = None
-        self.time_start = time.time()
         self.job = job
         devices.torch_gc()
         log.info("Starting job %s", job)

modules/sub_quadratic_attention.py

@@ -15,7 +15,7 @@ import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 import math
-from typing import Optional, NamedTuple, List
+from typing import Optional, NamedTuple
 
 
 def narrow_trunc(
@@ -97,7 +97,7 @@ def _query_chunk_attention(
         )
         return summarize_chunk(query, key_chunk, value_chunk)
 
-    chunks: List[AttnChunk] = [
+    chunks: list[AttnChunk] = [
         chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
     ]
     acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))

modules/sysinfo.py

@@ -1,7 +1,6 @@
 import json
 import os
 import sys
-import traceback
 
 import platform
 import hashlib
@@ -84,7 +83,7 @@ def get_dict():
         "Checksum": checksum_token,
         "Commandline": get_argv(),
         "Torch env info": get_torch_sysinfo(),
-        "Exceptions": get_exceptions(),
+        "Exceptions": errors.get_exceptions(),
         "CPU": {
             "model": platform.processor(),
             "count logical": psutil.cpu_count(logical=True),
@@ -104,21 +103,6 @@ def get_dict():
     return res
 
 
-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:
-        return list(reversed(errors.exception_records))
-    except Exception as e:
-        return str(e)
-
-
 def get_environment():
     return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
 

modules/textual_inversion/textual_inversion.py

@@ -181,40 +181,7 @@ class EmbeddingDatabase:
         else:
             return
 
-
+        embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
-        # 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]
-            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.")
-
-        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 = vectors
-        embedding.shape = shape
-        embedding.filename = path
-        embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
 
         if self.expected_shape == -1 or self.expected_shape == embedding.shape:
             self.register_embedding(embedding, shared.sd_model)
@@ -313,6 +280,45 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
     return fn
 
 
+def create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
+    if 'string_to_param' in data:  # textual inversion embeddings
+        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]
+        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.")
+
+    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 = vectors
+    embedding.shape = shape
+
+    if filepath:
+        embedding.filename = filepath
+        embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')
+
+    return embedding
+
+
 def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
@@ -386,7 +392,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
         assert log_directory, "Log directory is empty"
 
 
-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):
+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_name, preview_cfg_scale, preview_seed, preview_width, preview_height):
     from modules import processing
 
     save_embedding_every = save_embedding_every or 0
@@ -590,7 +596,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         p.prompt = preview_prompt
                         p.negative_prompt = preview_negative_prompt
                         p.steps = preview_steps
-                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+                        p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
                         p.cfg_scale = preview_cfg_scale
                         p.seed = preview_seed
                         p.width = preview_width

modules/txt2img.py

@@ -3,7 +3,7 @@ from contextlib import closing
 import modules.scripts
 from modules import processing
 from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.shared import opts, cmd_opts
+from modules.shared import opts
 import modules.shared as shared
 from modules.ui import plaintext_to_html
 import gradio as gr
@@ -45,7 +45,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
 
     p.user = request.username
 
-    if cmd_opts.enable_console_prompts:
+    if shared.opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
 
     with closing(p):

modules/ui.py

@@ -4,6 +4,7 @@ import os
 import sys
 from functools import reduce
 import warnings
+from contextlib import ExitStack
 
 import gradio as gr
 import gradio.utils
@@ -12,7 +13,7 @@ from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
 from modules import gradio_extensons  # noqa: F401
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
@@ -25,7 +26,6 @@ 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.images
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
 from modules.generation_parameters_copypaste import image_from_url_text
@@ -151,11 +151,15 @@ def connect_clear_prompt(button):
     )
 
 
-def update_token_counter(text, steps):
+def update_token_counter(text, steps, *, is_positive=True):
     try:
         text, _ = extra_networks.parse_prompt(text)
 
-        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        if is_positive:
+            _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        else:
+            prompt_flat_list = [text]
+
         prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
 
     except Exception:
@@ -169,76 +173,9 @@ def update_token_counter(text, steps):
     return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
 
 
-class Toprow:
+def update_negative_prompt_token_counter(text, steps):
-    """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
+    return update_token_counter(text, steps, is_positive=False)
 
-    def __init__(self, is_img2img):
-        id_part = "img2img" if is_img2img else "txt2img"
-        self.id_part = id_part
-
-        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)
-
-                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"])
-
-            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")
-
-            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')
-
-                    self.skip.click(
-                        fn=lambda: shared.state.skip(),
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                    self.interrupt.click(
-                        fn=lambda: shared.state.interrupt(),
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                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.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
-
-                    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")
-
-                    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],
-                    )
-
-                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):
@@ -278,8 +215,8 @@ def apply_setting(key, value):
     return getattr(opts, key)
 
 
-def create_output_panel(tabname, outdir):
+def create_output_panel(tabname, outdir, toprow=None):
-    return ui_common.create_output_panel(tabname, outdir)
+    return ui_common.create_output_panel(tabname, outdir, toprow)
 
 
 def create_sampler_and_steps_selection(choices, tabname):
@@ -326,7 +263,7 @@ def create_ui():
     scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
 
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        toprow = Toprow(is_img2img=False)
+        toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
 
         dummy_component = gr.Label(visible=False)
 
@@ -334,10 +271,17 @@ def create_ui():
         extra_tabs.__enter__()
 
         with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
-            with gr.Column(variant='compact', elem_id="txt2img_settings"):
+            with ExitStack() as stack:
+                if shared.opts.txt2img_settings_accordion:
+                    stack.enter_context(gr.Accordion("Open for Settings", open=False))
+                stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
+
                 scripts.scripts_txt2img.prepare_ui()
 
                 for category in ordered_ui_categories():
+                    if category == "prompt":
+                        toprow.create_inline_toprow_prompts()
+
                     if category == "sampler":
                         steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
 
@@ -348,7 +292,7 @@ def create_ui():
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
                             with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
 
                             if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="txt2img_column_batch"):
@@ -432,7 +376,7 @@ def create_ui():
                     show_progress=False,
                 )
 
-            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
 
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
@@ -533,7 +477,7 @@ def create_ui():
             ]
 
             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])
+            toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
 
         extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
         ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
@@ -544,13 +488,17 @@ def create_ui():
     scripts.scripts_img2img.initialize_scripts(is_img2img=True)
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        toprow = Toprow(is_img2img=True)
+        toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
 
         extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
         extra_tabs.__enter__()
 
         with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
-            with gr.Column(variant='compact', elem_id="img2img_settings"):
+            with ExitStack() as stack:
+                if shared.opts.img2img_settings_accordion:
+                    stack.enter_context(gr.Accordion("Open for Settings", open=False))
+                stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
+
                 copy_image_buttons = []
                 copy_image_destinations = {}
 
@@ -567,85 +515,89 @@ def create_ui():
                             button = gr.Button(title)
                             copy_image_buttons.append((button, name, elem))
 
-                with gr.Tabs(elem_id="mode_img2img"):
-                    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", 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="RGB", 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", 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="RGB", 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)
-
-                        def update_orig(image, state):
-                            if image is not None:
-                                same_size = state is not None and state.size == image.size
-                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                edited = same_size and has_exact_match
-                                return image if not edited or state is None else state
-
-                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
-
-                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
-
-                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
-                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(
-                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
-                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
-                            f"{hidden}</p>"
-                        )
-                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
-                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
-                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
-                        with gr.Accordion("PNG info", open=False):
-                            img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
-                            img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
-                            img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
-
-                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-
-                    for i, tab in enumerate(img2img_tabs):
-                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
-
-                def copy_image(img):
-                    if isinstance(img, dict) and 'image' in img:
-                        return img['image']
-
-                    return img
-
-                for button, name, elem in copy_image_buttons:
-                    button.click(
-                        fn=copy_image,
-                        inputs=[elem],
-                        outputs=[copy_image_destinations[name]],
-                    )
-                    button.click(
-                        fn=lambda: None,
-                        _js=f"switch_to_{name.replace(' ', '_')}",
-                        inputs=[],
-                        outputs=[],
-                    )
-
-                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")
-
                 scripts.scripts_img2img.prepare_ui()
 
                 for category in ordered_ui_categories():
+                    if category == "prompt":
+                        toprow.create_inline_toprow_prompts()
+
+                    if category == "image":
+                        with gr.Tabs(elem_id="mode_img2img"):
+                            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", 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="RGB", 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", 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="RGB", 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)
+
+                                def update_orig(image, state):
+                                    if image is not None:
+                                        same_size = state is not None and state.size == image.size
+                                        has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                        edited = same_size and has_exact_match
+                                        return image if not edited or state is None else state
+
+                                inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+                            with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+                                init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+                                init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
+
+                            with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+                                hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+                                gr.HTML(
+                                    "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                                    "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                                    f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+                                    f"{hidden}</p>"
+                                )
+                                img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+                                img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+                                img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+                                with gr.Accordion("PNG info", open=False):
+                                    img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+                                    img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+                                    img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+                            img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+                            for i, tab in enumerate(img2img_tabs):
+                                tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+                        def copy_image(img):
+                            if isinstance(img, dict) and 'image' in img:
+                                return img['image']
+
+                            return img
+
+                        for button, name, elem in copy_image_buttons:
+                            button.click(
+                                fn=copy_image,
+                                inputs=[elem],
+                                outputs=[copy_image_destinations[name]],
+                            )
+                            button.click(
+                                fn=lambda: None,
+                                _js=f"switch_to_{name.replace(' ', '_')}",
+                                inputs=[],
+                                outputs=[],
+                            )
+
+                        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")
+
                     if category == "sampler":
                         steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
 
@@ -661,8 +613,8 @@ def create_ui():
                                                 width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
                                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
                                             with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
-                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+                                                detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
 
                                     with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
                                         scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
@@ -683,12 +635,6 @@ def create_ui():
                                     scale_by.release(**on_change_args)
                                     button_update_resize_to.click(**on_change_args)
 
-                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
-                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
-                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
-                                    for component in [init_img, sketch]:
-                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
-
                             tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
                             tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
 
@@ -749,20 +695,26 @@ def create_ui():
                                 with gr.Column(scale=4):
                                     inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
 
-                            def select_img2img_tab(tab):
-                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
-
-                            for i, elem in enumerate(img2img_tabs):
-                                elem.select(
-                                    fn=lambda tab=i: select_img2img_tab(tab),
-                                    inputs=[],
-                                    outputs=[inpaint_controls, mask_alpha],
-                                )
-
                     if category not in {"accordions"}:
                         scripts.scripts_img2img.setup_ui_for_section(category)
 
-            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+            # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+            # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+            # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+            for component in [init_img, sketch]:
+                component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+            def select_img2img_tab(tab):
+                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+            for i, elem in enumerate(img2img_tabs):
+                elem.select(
+                    fn=lambda tab=i: select_img2img_tab(tab),
+                    inputs=[],
+                    outputs=[inpaint_controls, mask_alpha],
+                )
+
+            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
 
             img2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
@@ -1295,7 +1247,7 @@ def create_ui():
 
             loadsave.setup_ui()
 
-        if os.path.exists(os.path.join(script_path, "notification.mp3")):
+        if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
             gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
         footer = shared.html("footer.html")
@@ -1347,7 +1299,6 @@ checkpoint: <a id="sd_checkpoint_hash">N/A</a>
 
 def setup_ui_api(app):
     from pydantic import BaseModel, Field
-    from typing import List
 
     class QuicksettingsHint(BaseModel):
         name: str = Field(title="Name of the quicksettings field")
@@ -1356,7 +1307,7 @@ def setup_ui_api(app):
     def quicksettings_hint():
         return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
 
-    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
+    app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
 
     app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
 
@@ -1366,7 +1317,7 @@ def setup_ui_api(app):
         from fastapi.responses import PlainTextResponse
 
         text = sysinfo.get()
-        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+        filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
 
         return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
 

modules/ui_common.py

@@ -104,7 +104,7 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
 
 
-def create_output_panel(tabname, outdir):
+def create_output_panel(tabname, outdir, toprow=None):
 
     def open_folder(f):
         if not os.path.exists(f):
@@ -130,12 +130,15 @@ Requested path was: {f}
             else:
                 sp.Popen(["xdg-open", path])
 
-    with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
+    with gr.Column(elem_id=f"{tabname}_results"):
-        with gr.Group(elem_id=f"{tabname}_gallery_container"):
+        if toprow:
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
+            toprow.create_inline_toprow_image()
 
-        generation_info = None
+        with gr.Column(variant='panel', elem_id=f"{tabname}_results_panel"):
-        with gr.Column():
+            with gr.Group(elem_id=f"{tabname}_gallery_container"):
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
+
+            generation_info = None
             with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
                 open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")
 

modules/ui_extensions.py

@@ -65,7 +65,7 @@ def save_config_state(name):
     filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
     print(f"Saving backup of webui/extension state to {filename}.")
     with open(filename, "w", encoding="utf-8") as f:
-        json.dump(current_config_state, f, indent=4)
+        json.dump(current_config_state, f, indent=4, ensure_ascii=False)
     config_states.list_config_states()
     new_value = next(iter(config_states.all_config_states.keys()), "Current")
     new_choices = ["Current"] + list(config_states.all_config_states.keys())
@@ -197,7 +197,7 @@ def update_config_states_table(state_name):
         config_state = config_states.all_config_states[state_name]
 
     config_name = config_state.get("name", "Config")
-    created_date = time.asctime(time.gmtime(config_state["created_at"]))
+    created_date = datetime.fromtimestamp(config_state["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
     filepath = config_state.get("filepath", "<unknown>")
 
     try:

modules/ui_extra_networks.py

@@ -1,3 +1,4 @@
+import functools
 import os.path
 import urllib.parse
 from pathlib import Path
@@ -15,6 +16,17 @@ from modules.ui_components import ToolButton
 extra_pages = []
 allowed_dirs = set()
 
+default_allowed_preview_extensions = ["png", "jpg", "jpeg", "webp", "gif"]
+
+
+@functools.cache
+def allowed_preview_extensions_with_extra(extra_extensions=None):
+    return set(default_allowed_preview_extensions) | set(extra_extensions or [])
+
+
+def allowed_preview_extensions():
+    return allowed_preview_extensions_with_extra((shared.opts.samples_format, ))
+
 
 def register_page(page):
     """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
@@ -33,9 +45,9 @@ def fetch_file(filename: str = ""):
     if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
         raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
 
-    ext = os.path.splitext(filename)[1].lower()
+    ext = os.path.splitext(filename)[1].lower()[1:]
-    if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
+    if ext not in allowed_preview_extensions():
-        raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
+        raise ValueError(f"File cannot be fetched: {filename}. Extensions allowed: {allowed_preview_extensions()}.")
 
     # would profit from returning 304
     return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@@ -91,6 +103,7 @@ class ExtraNetworksPage:
         self.name = title.lower()
         self.id_page = self.name.replace(" ", "_")
         self.card_page = shared.html("extra-networks-card.html")
+        self.allow_prompt = True
         self.allow_negative_prompt = False
         self.metadata = {}
         self.items = {}
@@ -213,9 +226,9 @@ class ExtraNetworksPage:
         metadata_button = ""
         metadata = item.get("metadata")
         if metadata:
-            metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(item['name'])})'></div>"
+            metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(html.escape(item['name']))})'></div>"
 
-        edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(item['name'])})'></div>"
+        edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(html.escape(item['name']))})'></div>"
 
         local_path = ""
         filename = item.get("filename", "")
@@ -235,7 +248,7 @@ class ExtraNetworksPage:
         if search_only and shared.opts.extra_networks_hidden_models == "Never":
             return ""
 
-        sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
+        sort_keys = " ".join([f'data-sort-{k}="{html.escape(str(v))}"' for k, v in item.get("sort_keys", {}).items()]).strip()
 
         args = {
             "background_image": background_image,
@@ -266,6 +279,7 @@ class ExtraNetworksPage:
             "date_created": int(stat.st_ctime or 0),
             "date_modified": int(stat.st_mtime or 0),
             "name": pth.name.lower(),
+            "path": str(pth.parent).lower(),
         }
 
     def find_preview(self, path):
@@ -273,11 +287,7 @@ class ExtraNetworksPage:
         Find a preview PNG for a given path (without extension) and call link_preview on it.
         """
 
-        preview_extensions = ["png", "jpg", "jpeg", "webp"]
+        potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in allowed_preview_extensions()], [])
-        if shared.opts.samples_format not in preview_extensions:
-            preview_extensions.append(shared.opts.samples_format)
-
-        potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
 
         for file in potential_files:
             if os.path.isfile(file):
@@ -359,7 +369,10 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
     related_tabs = []
 
     for page in ui.stored_extra_pages:
-        with gr.Tab(page.title, id=page.id_page) as tab:
+        with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
+            with gr.Column(elem_id=f"{tabname}_{page.id_page}_prompts", elem_classes=["extra-page-prompts"]):
+                pass
+
             elem_id = f"{tabname}_{page.id_page}_cards_html"
             page_elem = gr.HTML('Loading...', elem_id=elem_id)
             ui.pages.append(page_elem)
@@ -373,19 +386,28 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
             related_tabs.append(tab)
 
     edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
-    dropdown_sort = gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
+    dropdown_sort = gr.Dropdown(choices=['Path', 'Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
-    button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes="sortorder", visible=False)
+    button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
     button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
     checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
 
     ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
     ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
 
-    for tab in unrelated_tabs:
+    tab_controls = [edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs]
-        tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
 
-    for tab in related_tabs:
+    for tab in unrelated_tabs:
-        tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
+        tab.select(fn=lambda: [gr.update(visible=False) for _ in tab_controls], _js='function(){ extraNetworksUrelatedTabSelected("' + tabname + '"); }', inputs=[], outputs=tab_controls, show_progress=False)
+
+    for page, tab in zip(ui.stored_extra_pages, related_tabs):
+        allow_prompt = "true" if page.allow_prompt else "false"
+        allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
+
+        jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
+
+        tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls],  _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)
+
+    dropdown_sort.change(fn=lambda: None, _js="function(){ applyExtraNetworkSort('" + tabname + "'); }")
 
     def pages_html():
         if not ui.pages_contents:

modules/ui_extra_networks_checkpoints.py

@@ -10,11 +10,16 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
     def __init__(self):
         super().__init__('Checkpoints')
 
+        self.allow_prompt = False
+
     def refresh(self):
         shared.refresh_checkpoints()
 
     def create_item(self, name, index=None, enable_filter=True):
         checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
+        if checkpoint is None:
+            return
+
         path, ext = os.path.splitext(checkpoint.filename)
         return {
             "name": checkpoint.name_for_extra,
@@ -30,9 +35,12 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
         }
 
     def list_items(self):
+        # instantiate a list to protect against concurrent modification
         names = list(sd_models.checkpoints_list)
         for index, name in enumerate(names):
-            yield self.create_item(name, index)
+            item = self.create_item(name, index)
+            if item is not None:
+                yield item
 
     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]

modules/ui_extra_networks_hypernets.py

@@ -13,7 +13,10 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
         shared.reload_hypernetworks()
 
     def create_item(self, name, index=None, enable_filter=True):
-        full_path = shared.hypernetworks[name]
+        full_path = shared.hypernetworks.get(name)
+        if full_path is None:
+            return
+
         path, ext = os.path.splitext(full_path)
         sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
         shorthash = sha256[0:10] if sha256 else None
@@ -31,8 +34,12 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
         }
 
     def list_items(self):
-        for index, name in enumerate(shared.hypernetworks):
+        # instantiate a list to protect against concurrent modification
-            yield self.create_item(name, index)
+        names = list(shared.hypernetworks)
+        for index, name in enumerate(names):
+            item = self.create_item(name, index)
+            if item is not None:
+                yield item
 
     def allowed_directories_for_previews(self):
         return [shared.cmd_opts.hypernetwork_dir]

modules/ui_extra_networks_textual_inversion.py

@@ -14,6 +14,8 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
 
     def create_item(self, name, index=None, enable_filter=True):
         embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
+        if embedding is None:
+            return
 
         path, ext = os.path.splitext(embedding.filename)
         return {
@@ -29,8 +31,12 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
         }
 
     def list_items(self):
-        for index, name in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings):
+        # instantiate a list to protect against concurrent modification
-            yield self.create_item(name, index)
+        names = list(sd_hijack.model_hijack.embedding_db.word_embeddings)
+        for index, name in enumerate(names):
+            item = self.create_item(name, index)
+            if item is not None:
+                yield item
 
     def allowed_directories_for_previews(self):
         return list(sd_hijack.model_hijack.embedding_db.embedding_dirs)

modules/ui_extra_networks_user_metadata.py

@@ -134,7 +134,7 @@ class UserMetadataEditor:
         basename, ext = os.path.splitext(filename)
 
         with open(basename + '.json', "w", encoding="utf8") as file:
-            json.dump(metadata, file, indent=4)
+            json.dump(metadata, file, indent=4, ensure_ascii=False)
 
     def save_user_metadata(self, name, desc, notes):
         user_metadata = self.get_user_metadata(name)

modules/ui_gradio_extensions.py

@@ -2,12 +2,12 @@ import os
 import gradio as gr
 
 from modules import localization, shared, scripts
-from modules.paths import script_path, data_path
+from modules.paths import script_path, data_path, cwd
 
 
 def webpath(fn):
-    if fn.startswith(script_path):
+    if fn.startswith(cwd):
-        web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+        web_path = os.path.relpath(fn, cwd)
     else:
         web_path = os.path.abspath(fn)
 

modules/ui_loadsave.py

@@ -4,7 +4,7 @@ import os
 import gradio as gr
 
 from modules import errors
-from modules.ui_components import ToolButton
+from modules.ui_components import ToolButton, InputAccordion
 
 
 def radio_choices(comp):  # gradio 3.41 changes choices from list of values to list of pairs
@@ -32,8 +32,6 @@ class UiLoadsave:
             self.error_loading = True
             errors.display(e, "loading settings")
 
-
-
     def add_component(self, path, x):
         """adds component to the registry of tracked components"""
 
@@ -43,20 +41,24 @@ class UiLoadsave:
             key = f"{path}/{field}"
 
             if getattr(obj, 'custom_script_source', None) is not None:
-              key = f"customscript/{obj.custom_script_source}/{key}"
+                key = f"customscript/{obj.custom_script_source}/{key}"
 
             if getattr(obj, 'do_not_save_to_config', False):
                 return
 
             saved_value = self.ui_settings.get(key, None)
+
+            if isinstance(obj, gr.Accordion) and isinstance(x, InputAccordion) and field == 'value':
+                field = 'open'
+
             if saved_value is None:
                 self.ui_settings[key] = getattr(obj, field)
             elif condition and not condition(saved_value):
                 pass
             else:
-                if isinstance(x, gr.Textbox) and field == 'value':  # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
+                if isinstance(obj, gr.Textbox) and field == 'value':  # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
                     saved_value = str(saved_value)
-                elif isinstance(x, gr.Number) and field == 'value':
+                elif isinstance(obj, gr.Number) and field == 'value':
                     try:
                         saved_value = float(saved_value)
                     except ValueError:
@@ -67,7 +69,7 @@ class UiLoadsave:
                     init_field(saved_value)
 
             if field == 'value' and key not in self.component_mapping:
-                self.component_mapping[key] = x
+                self.component_mapping[key] = obj
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton, gr.Button] and x.visible:
             apply_field(x, 'visible')
@@ -100,6 +102,12 @@ class UiLoadsave:
 
             apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
 
+        if type(x) == InputAccordion:
+            if x.accordion.visible:
+                apply_field(x.accordion, 'visible')
+            apply_field(x, 'value')
+            apply_field(x.accordion, 'value')
+
         def check_tab_id(tab_id):
             tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
             if type(tab_id) == str:
@@ -133,7 +141,7 @@ class UiLoadsave:
 
     def write_to_file(self, current_ui_settings):
         with open(self.filename, "w", encoding="utf8") as file:
-            json.dump(current_ui_settings, file, indent=4)
+            json.dump(current_ui_settings, file, indent=4, ensure_ascii=False)
 
     def dump_defaults(self):
         """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""

modules/ui_prompt_styles.py

@@ -4,6 +4,7 @@ from modules import shared, ui_common, ui_components, styles
 
 styles_edit_symbol = '\U0001f58c\uFE0F'  # 🖌️
 styles_materialize_symbol = '\U0001f4cb'  # 📋
+styles_copy_symbol = '\U0001f4dd'  # 📝
 
 
 def select_style(name):
@@ -52,6 +53,8 @@ def refresh_styles():
 class UiPromptStyles:
     def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
         self.tabname = tabname
+        self.main_ui_prompt = main_ui_prompt
+        self.main_ui_negative_prompt = main_ui_negative_prompt
 
         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")
@@ -61,13 +64,14 @@ class UiPromptStyles:
             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.")
+                self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply_dialog", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+                self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
 
             with gr.Row():
-                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
+                self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3, elem_classes=["prompt"])
 
             with gr.Row():
-                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
+                self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3, elem_classes=["prompt"])
 
             with gr.Row():
                 self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
@@ -96,15 +100,21 @@ class UiPromptStyles:
             show_progress=False,
         ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
 
-        self.materialize.click(
+        self.setup_apply_button(self.materialize)
-            fn=materialize_styles,
+
-            inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+        self.copy.click(
-            outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+            fn=lambda p, n: (p, n),
+            inputs=[main_ui_prompt, main_ui_negative_prompt],
+            outputs=[self.prompt, self.neg_prompt],
             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)
 
-
+    def setup_apply_button(self, button):
-
+        button.click(
-
+            fn=materialize_styles,
+            inputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
+            outputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
+            show_progress=False,
+        ).then(fn=None, _js="function(){update_"+self.tabname+"_tokens(); closePopup();}", show_progress=False)

modules/ui_settings.py

@@ -1,10 +1,11 @@
 import gradio as gr
 
-from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
+from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo, timer
 from modules.call_queue import wrap_gradio_call
 from modules.shared import opts
 from modules.ui_components import FormRow
 from modules.ui_gradio_extensions import reload_javascript
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 
 def get_value_for_setting(key):
@@ -63,6 +64,9 @@ class UiSettings:
     quicksettings_list = None
     quicksettings_names = None
     text_settings = None
+    show_all_pages = None
+    show_one_page = None
+    search_input = None
 
     def run_settings(self, *args):
         changed = []
@@ -135,7 +139,7 @@ class UiSettings:
                         gr.Group()
                         current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
                         current_tab.__enter__()
-                        current_row = gr.Column(variant='compact')
+                        current_row = gr.Column(elem_id=f"column_settings_{elem_id}", variant='compact')
                         current_row.__enter__()
 
                         previous_section = item.section
@@ -173,26 +177,43 @@ class UiSettings:
                     download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                     reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
                     with gr.Row():
-                        unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+                        unload_sd_model = gr.Button(value='Unload SD checkpoint to RAM', elem_id="sett_unload_sd_model")
-                        reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+                        reload_sd_model = gr.Button(value='Load SD checkpoint to VRAM from RAM', elem_id="sett_reload_sd_model")
+                    with gr.Row():
+                        calculate_all_checkpoint_hash = gr.Button(value='Calculate hash for all checkpoint', elem_id="calculate_all_checkpoint_hash")
+                        calculate_all_checkpoint_hash_threads = gr.Number(value=1, label="Number of parallel calculations", elem_id="calculate_all_checkpoint_hash_threads", precision=0, minimum=1)
 
                 with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
                     gr.HTML(shared.html("licenses.html"), elem_id="licenses")
 
-                gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+                self.show_all_pages = gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+                self.show_one_page = gr.Button(value="Show only one page", elem_id="settings_show_one_page", visible=False)
+                self.show_one_page.click(lambda: None)
+
+                self.search_input = gr.Textbox(value="", elem_id="settings_search", max_lines=1, placeholder="Search...", show_label=False)
 
                 self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
 
+            def call_func_and_return_text(func, text):
+                def handler():
+                    t = timer.Timer()
+                    func()
+                    t.record(text)
+
+                    return f'{text} in {t.total:.1f}s'
+
+                return handler
+
             unload_sd_model.click(
-                fn=sd_models.unload_model_weights,
+                fn=call_func_and_return_text(sd_models.unload_model_weights, 'Unloaded the checkpoint'),
                 inputs=[],
-                outputs=[]
+                outputs=[self.result]
             )
 
             reload_sd_model.click(
-                fn=sd_models.reload_model_weights,
+                fn=call_func_and_return_text(lambda: sd_models.send_model_to_device(shared.sd_model), 'Loaded the checkpoint'),
                 inputs=[],
-                outputs=[]
+                outputs=[self.result]
             )
 
             request_notifications.click(
@@ -241,6 +262,21 @@ class UiSettings:
                 outputs=[sysinfo_check_output],
             )
 
+            def calculate_all_checkpoint_hash_fn(max_thread):
+                checkpoints_list = sd_models.checkpoints_list.values()
+                with ThreadPoolExecutor(max_workers=max_thread) as executor:
+                    futures = [executor.submit(checkpoint.calculate_shorthash) for checkpoint in checkpoints_list]
+                    completed = 0
+                    for _ in as_completed(futures):
+                        completed += 1
+                        print(f"{completed} / {len(checkpoints_list)} ")
+                    print("Finish calculating hash for all checkpoints")
+
+            calculate_all_checkpoint_hash.click(
+                fn=calculate_all_checkpoint_hash_fn,
+                inputs=[calculate_all_checkpoint_hash_threads],
+            )
+
         self.interface = settings_interface
 
     def add_quicksettings(self):
@@ -294,3 +330,8 @@ class UiSettings:
             outputs=[self.component_dict[k] for k in component_keys],
             queue=False,
         )
+
+    def search(self, text):
+        print(text)
+
+        return [gr.update(visible=text in (comp.label or "")) for comp in self.components]

modules/ui_toprow.py

@@ -0,0 +1,141 @@
+import gradio as gr
+
+from modules import shared, ui_prompt_styles
+import modules.images
+
+from modules.ui_components import ToolButton
+
+
+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"""
+
+    prompt = None
+    prompt_img = None
+    negative_prompt = None
+
+    button_interrogate = None
+    button_deepbooru = None
+
+    interrupt = None
+    skip = None
+    submit = None
+
+    paste = None
+    clear_prompt_button = None
+    apply_styles = None
+    restore_progress_button = None
+
+    token_counter = None
+    token_button = None
+    negative_token_counter = None
+    negative_token_button = None
+
+    ui_styles = None
+
+    submit_box = None
+
+    def __init__(self, is_img2img, is_compact=False):
+        id_part = "img2img" if is_img2img else "txt2img"
+        self.id_part = id_part
+        self.is_img2img = is_img2img
+        self.is_compact = is_compact
+
+        if not is_compact:
+            with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+                self.create_classic_toprow()
+        else:
+            self.create_submit_box()
+
+    def create_classic_toprow(self):
+        self.create_prompts()
+
+        with gr.Column(scale=1, elem_id=f"{self.id_part}_actions_column"):
+            self.create_submit_box()
+
+            self.create_tools_row()
+
+            self.create_styles_ui()
+
+    def create_inline_toprow_prompts(self):
+        if not self.is_compact:
+            return
+
+        self.create_prompts()
+
+        with gr.Row(elem_classes=["toprow-compact-stylerow"]):
+            with gr.Column(elem_classes=["toprow-compact-tools"]):
+                self.create_tools_row()
+            with gr.Column():
+                self.create_styles_ui()
+
+    def create_inline_toprow_image(self):
+        if not self.is_compact:
+            return
+
+        self.submit_box.render()
+
+    def create_prompts(self):
+        with gr.Column(elem_id=f"{self.id_part}_prompt_container", elem_classes=["prompt-container-compact"] if self.is_compact else [], scale=6):
+            with gr.Row(elem_id=f"{self.id_part}_prompt_row", elem_classes=["prompt-row"]):
+                self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.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"{self.id_part}_prompt_image", file_count="single", type="binary", visible=False)
+
+            with gr.Row(elem_id=f"{self.id_part}_neg_prompt_row", elem_classes=["prompt-row"]):
+                self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+        self.prompt_img.change(
+            fn=modules.images.image_data,
+            inputs=[self.prompt_img],
+            outputs=[self.prompt, self.prompt_img],
+            show_progress=False,
+        )
+
+    def create_submit_box(self):
+        with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
+            self.submit_box = submit_box
+
+            self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
+            self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
+            self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
+
+            self.skip.click(
+                fn=lambda: shared.state.skip(),
+                inputs=[],
+                outputs=[],
+            )
+
+            self.interrupt.click(
+                fn=lambda: shared.state.interrupt(),
+                inputs=[],
+                outputs=[],
+            )
+
+    def create_tools_row(self):
+        with gr.Row(elem_id=f"{self.id_part}_tools"):
+            from modules.ui import paste_symbol, clear_prompt_symbol, restore_progress_symbol
+
+            self.paste = ToolButton(value=paste_symbol, elem_id="paste", tooltip="Read generation parameters from prompt or last generation if prompt is empty into user interface.")
+            self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{self.id_part}_clear_prompt", tooltip="Clear prompt")
+            self.apply_styles = ToolButton(value=ui_prompt_styles.styles_materialize_symbol, elem_id=f"{self.id_part}_style_apply", tooltip="Apply all selected styles to prompts.")
+
+            if self.is_img2img:
+                self.button_interrogate = ToolButton('📎', tooltip='Interrogate CLIP - use CLIP neural network to create a text describing the image, and put it into the prompt field', elem_id="interrogate")
+                self.button_deepbooru = ToolButton('📦', tooltip='Interrogate DeepBooru - use DeepBooru neural network to create a text describing the image, and put it into the prompt field', elem_id="deepbooru")
+
+            self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
+
+            self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"])
+            self.token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_token_button")
+            self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"])
+            self.negative_token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_negative_token_button")
+
+            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],
+            )
+
+    def create_styles_ui(self):
+        self.ui_styles = ui_prompt_styles.UiPromptStyles(self.id_part, self.prompt, self.negative_prompt)
+        self.ui_styles.setup_apply_button(self.apply_styles)

modules/xlmr_m18.py

@@ -0,0 +1,164 @@
+from transformers import BertPreTrainedModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    config_class = BertSeriesConfig
+
+    def __init__(self, config=None, **kargs):
+        # modify initialization for autoloading
+        if config is None:
+            config = XLMRobertaConfig()
+            config.attention_probs_dropout_prob= 0.1
+            config.bos_token_id=0
+            config.eos_token_id=2
+            config.hidden_act='gelu'
+            config.hidden_dropout_prob=0.1
+            config.hidden_size=1024
+            config.initializer_range=0.02
+            config.intermediate_size=4096
+            config.layer_norm_eps=1e-05
+            config.max_position_embeddings=514
+
+            config.num_attention_heads=16
+            config.num_hidden_layers=24
+            config.output_past=True
+            config.pad_token_id=1
+            config.position_embedding_type= "absolute"
+
+            config.type_vocab_size= 1
+            config.use_cache=True
+            config.vocab_size= 250002
+            config.project_dim = 1024
+            config.learn_encoder = False
+        super().__init__(config)
+        self.roberta = XLMRobertaModel(config)
+        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+        # self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        # self.pooler = lambda x: x[:,0]
+        # self.post_init()
+
+        self.has_pre_transformation = True
+        if self.has_pre_transformation:
+            self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
+            self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_init()
+
+    def encode(self,c):
+        device = next(self.parameters()).device
+        text = self.tokenizer(c,
+                        truncation=True,
+                        max_length=77,
+                        return_length=False,
+                        return_overflowing_tokens=False,
+                        padding="max_length",
+                        return_tensors="pt")
+        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+        text["attention_mask"] = torch.tensor(
+            text['attention_mask']).to(device)
+        features = self(**text)
+        return features['projection_state']
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) :
+        r"""
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=return_dict,
+        )
+
+        # # last module outputs
+        # sequence_output = outputs[0]
+
+
+        # # project every module
+        # sequence_output_ln = self.pre_LN(sequence_output)
+
+        # # pooler
+        # pooler_output = self.pooler(sequence_output_ln)
+        # pooler_output = self.transformation(pooler_output)
+        # projection_state = self.transformation(outputs.last_hidden_state)
+
+        if self.has_pre_transformation:
+            sequence_output2 = outputs["hidden_states"][-2]
+            sequence_output2 = self.pre_LN(sequence_output2)
+            projection_state2 = self.transformation_pre(sequence_output2)
+
+            return {
+                "projection_state": projection_state2,
+                "last_hidden_state": outputs.last_hidden_state,
+                "hidden_states": outputs.hidden_states,
+                "attentions": outputs.attentions,
+            }
+        else:
+            projection_state = self.transformation(outputs.last_hidden_state)
+            return {
+                "projection_state": projection_state,
+                "last_hidden_state": outputs.last_hidden_state,
+                "hidden_states": outputs.hidden_states,
+                "attentions": outputs.attentions,
+            }
+
+
+        # return {
+        #     'pooler_output':pooler_output,
+        #     'last_hidden_state':outputs.last_hidden_state,
+        #     'hidden_states':outputs.hidden_states,
+        #     'attentions':outputs.attentions,
+        #     'projection_state':projection_state,
+        #     'sequence_out': sequence_output
+        # }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+    base_model_prefix = 'roberta'
+    config_class= RobertaSeriesConfig

pyproject.toml

@@ -16,6 +16,7 @@ exclude = [
 
 ignore = [
 	"E501", # Line too long
+	"E721", # Do not compare types, use `isinstance`
 	"E731", # Do not assign a `lambda` expression, use a `def`
 	
 	"I001", # Import block is un-sorted or un-formatted

requirements_versions.txt

@@ -27,6 +27,6 @@ timm==0.9.2
 tomesd==0.1.3
 torch
 torchdiffeq==0.2.3
-torchsde==0.2.5
+torchsde==0.2.6
 transformers==4.30.2
 httpx==0.24.1

script.js

@@ -124,16 +124,29 @@ document.addEventListener("DOMContentLoaded", function() {
  * Add a ctrl+enter as a shortcut to start a generation
  */
 document.addEventListener('keydown', function(e) {
-    var handled = false;
+    const isEnter = e.key === 'Enter' || e.keyCode === 13;
-    if (e.key !== undefined) {
+    const isModifierKey = e.metaKey || e.ctrlKey || e.altKey;
-        if ((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
+
-    } else if (e.keyCode !== undefined) {
+    const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]');
-        if ((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
+    const generateButton = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
-    }
+
-    if (handled) {
+    if (isEnter && isModifierKey) {
-        var button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
+        if (interruptButton.style.display === 'block') {
-        if (button) {
+            interruptButton.click();
-            button.click();
+            const callback = (mutationList) => {
+                for (const mutation of mutationList) {
+                    if (mutation.type === 'attributes' && mutation.attributeName === 'style') {
+                        if (interruptButton.style.display === 'none') {
+                            generateButton.click();
+                            observer.disconnect();
+                        }
+                    }
+                }
+            };
+            const observer = new MutationObserver(callback);
+            observer.observe(interruptButton, {attributes: true});
+        } else {
+            generateButton.click();
         }
         e.preventDefault();
     }

scripts/postprocessing_upscale.py

@@ -29,7 +29,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
                                 upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
                                 upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
                             with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
+                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
                                 upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
             with FormRow():

scripts/prompts_from_file.py

@@ -5,11 +5,17 @@ import shlex
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import sd_samplers, errors
+from modules import sd_samplers, errors, sd_models
 from modules.processing import Processed, process_images
 from modules.shared import state
 
 
+def process_model_tag(tag):
+    info = sd_models.get_closet_checkpoint_match(tag)
+    assert info is not None, f'Unknown checkpoint: {tag}'
+    return info.name
+
+
 def process_string_tag(tag):
     return tag
 
@@ -27,7 +33,7 @@ def process_boolean_tag(tag):
 
 
 prompt_tags = {
-    "sd_model": None,
+    "sd_model": process_model_tag,
     "outpath_samples": process_string_tag,
     "outpath_grids": process_string_tag,
     "prompt_for_display": process_string_tag,
@@ -108,6 +114,7 @@ class Script(scripts.Script):
     def ui(self, is_img2img):
         checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
         checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
+        prompt_position = gr.Radio(["start", "end"], label="Insert prompts at the", elem_id=self.elem_id("prompt_position"), value="start")
 
         prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
         file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
@@ -118,9 +125,9 @@ class Script(scripts.Script):
         # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
         # be unclear to the user that shift-enter is needed.
         prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt], show_progress=False)
-        return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
+        return [checkbox_iterate, checkbox_iterate_batch, prompt_position, prompt_txt]
 
-    def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
+    def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_position, prompt_txt: str):
         lines = [x for x in (x.strip() for x in prompt_txt.splitlines()) if x]
 
         p.do_not_save_grid = True
@@ -156,7 +163,22 @@ class Script(scripts.Script):
 
             copy_p = copy.copy(p)
             for k, v in args.items():
-                setattr(copy_p, k, v)
+                if k == "sd_model":
+                    copy_p.override_settings['sd_model_checkpoint'] = v
+                else:
+                    setattr(copy_p, k, v)
+
+            if args.get("prompt") and p.prompt:
+                if prompt_position == "start":
+                    copy_p.prompt = args.get("prompt") + " " + p.prompt
+                else:
+                    copy_p.prompt = p.prompt + " " + args.get("prompt")
+
+            if args.get("negative_prompt") and p.negative_prompt:
+                if prompt_position == "start":
+                    copy_p.negative_prompt = args.get("negative_prompt") + " " + p.negative_prompt
+                else:
+                    copy_p.negative_prompt = p.negative_prompt + " " + args.get("negative_prompt")
 
             proc = process_images(copy_p)
             images += proc.images

scripts/xyz_grid.py

@@ -205,13 +205,14 @@ def csv_string_to_list_strip(data_str):
 
 
 class AxisOption:
-    def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
+    def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None, prepare=None):
         self.label = label
         self.type = type
         self.apply = apply
         self.format_value = format_value
         self.confirm = confirm
         self.cost = cost
+        self.prepare = prepare
         self.choices = choices
 
 
@@ -536,6 +537,8 @@ class Script(scripts.Script):
 
             if opt.choices is not None and not csv_mode:
                 valslist = vals_dropdown
+            elif opt.prepare is not None:
+                valslist = opt.prepare(vals)
             else:
                 valslist = csv_string_to_list_strip(vals)
 
@@ -773,6 +776,8 @@ class Script(scripts.Script):
                 # TODO: See previous comment about intentional data misalignment.
                 adj_g = g-1 if g > 0 else g
                 images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
+                if not include_sub_grids:  # if not include_sub_grids then skip saving after the first grid
+                    break
 
         if not include_sub_grids:
             # Done with sub-grids, drop all related information:

style.css

@@ -83,8 +83,10 @@ div.compact{
     white-space: nowrap;
 }
 
-.gradio-dropdown ul.options li.item {
+@media (pointer:fine) {
-    padding: 0.05em 0;
+    .gradio-dropdown ul.options li.item {
+        padding: 0.05em 0;
+    }
 }
 
 .gradio-dropdown ul.options li.item.selected {
@@ -202,6 +204,11 @@ div.block.gradio-accordion {
     padding: 8px 8px;
 }
 
+input[type="checkbox"].input-accordion-checkbox{
+    vertical-align: sub;
+    margin-right: 0.5em;
+}
+
 
 /* txt2img/img2img specific */
 
@@ -289,6 +296,13 @@ div.block.gradio-accordion {
     min-height: 4.5em;
 }
 
+#txt2img_generate, #img2img_generate {
+    min-height: 4.5em;
+}
+.generate-box-compact #txt2img_generate, .generate-box-compact #img2img_generate {
+    min-height: 3em;
+}
+
 @media screen and (min-width: 2500px) {
     #txt2img_gallery, #img2img_gallery {
         min-height: 768px;
@@ -396,6 +410,15 @@ div#extras_scale_to_tab div.form{
     min-width: 0.5em;
 }
 
+div.toprow-compact-stylerow{
+    margin: 0.5em 0;
+}
+
+div.toprow-compact-tools{
+    min-width: fit-content !important;
+    max-width: fit-content;
+}
+
 /* settings */
 #quicksettings {
     align-items: end;
@@ -421,6 +444,7 @@ div#extras_scale_to_tab div.form{
 #settings > div{
     border: none;
     margin-left: 10em;
+    padding: 0 var(--spacing-xl);
 }
 
 #settings > div.tab-nav{
@@ -435,6 +459,16 @@ div#extras_scale_to_tab div.form{
     border: none;
     text-align: left;
     white-space: initial;
+    padding: 4px;
+}
+
+#settings > div.tab-nav .settings-category{
+    display: block;
+    margin: 1em 0 0.25em 0;
+    font-weight: bold;
+    text-decoration: underline;
+    cursor: default;
+    user-select: none;
 }
 
 #settings_result{
@@ -516,7 +550,8 @@ table.popup-table .link{
     height: 20px;
     background: #b4c0cc;
     border-radius: 3px !important;
-    top: -20px;
+    top: -14px;
+    left: 0px;
     width: 100%;
 }
 
@@ -581,7 +616,6 @@ table.popup-table .link{
     width: 100%;
     height: 100%;
     overflow: auto;
-    background-color: rgba(20, 20, 20, 0.95);
 }
 
 .global-popup *{
@@ -590,9 +624,6 @@ table.popup-table .link{
 
 .global-popup-close:before {
     content: "×";
-}
-
-.global-popup-close{
     position: fixed;
     right: 0.25em;
     top: 0;
@@ -601,10 +632,20 @@ table.popup-table .link{
     font-size: 32pt;
 }
 
+.global-popup-close{
+    position: fixed;
+    left: 0;
+    top: 0;
+    width: 100%;
+    height: 100%;
+    background-color: rgba(20, 20, 20, 0.95);
+}
+
 .global-popup-inner{
     display: inline-block;
     margin: auto;
     padding: 2em;
+    z-index: 1001;
 }
 
 /* fullpage image viewer */
@@ -808,6 +849,18 @@ footer {
 
 /* extra networks UI */
 
+.extra-page > div.gap{
+    gap: 0;
+}
+
+.extra-page-prompts{
+    margin-bottom: 0;
+}
+
+.extra-page-prompts.extra-page-prompts-active{
+    margin-bottom: 1em;
+}
+
 .extra-network-cards{
     height: calc(100vh - 24rem);
     overflow: clip scroll;

webui.bat

@@ -1,6 +1,11 @@
 @echo off
 
+if exist webui.settings.bat (
+    call webui.settings.bat
+)
+
 if not defined PYTHON (set PYTHON=python)
+if defined GIT (set "GIT_PYTHON_GIT_EXECUTABLE=%GIT%")
 if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
 
 set SD_WEBUI_RESTART=tmp/restart

webui.py

@@ -74,7 +74,7 @@ def webui():
             if shared.opts.auto_launch_browser == "Remote" or cmd_opts.autolaunch:
                 auto_launch_browser = True
             elif shared.opts.auto_launch_browser == "Local":
-                auto_launch_browser = not any([cmd_opts.listen, cmd_opts.share, cmd_opts.ngrok, cmd_opts.server_name])
+                auto_launch_browser = not cmd_opts.webui_is_non_local
 
         app, local_url, share_url = shared.demo.launch(
             share=cmd_opts.share,

webui.sh

@@ -4,12 +4,6 @@
 # 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 )
 
 
@@ -28,6 +22,12 @@ then
     source "$SCRIPT_DIR"/webui-user.sh
 fi
 
+# If $venv_dir is "-", then disable venv support
+use_venv=1
+if [[ $venv_dir == "-" ]]; then
+  use_venv=0
+fi
+
 # Set defaults
 # Install directory without trailing slash
 if [[ -z "${install_dir}" ]]
@@ -51,6 +51,8 @@ fi
 if [[ -z "${GIT}" ]]
 then
     export GIT="git"
+else
+    export GIT_PYTHON_GIT_EXECUTABLE="${GIT}"
 fi
 
 # python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
@@ -87,7 +89,7 @@ delimiter="################################################################"
 
 printf "\n%s\n" "${delimiter}"
 printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
-printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
+printf "\e[1m\e[34mTested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer.\e[0m"
 printf "\n%s\n" "${delimiter}"
 
 # Do not run as root
@@ -141,9 +143,8 @@ case "$gpu_info" in
     *"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
     ;;
     *"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
-         export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6"
+         export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/test/rocm5.6"
-        # Navi 3 needs at least 5.5 which is only on the nightly chain, previous versions are no longer online (torch==2.1.0.dev-20230614+rocm5.5 torchvision==0.16.0.dev-20230614+rocm5.5 torchaudio==2.1.0.dev-20230614+rocm5.5)
+        # Navi 3 needs at least 5.5 which is only on the torch 2.1.0 release candidates right now
-        # so switch to nightly rocm5.6 without explicit versions this time
     ;;
     *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
         printf "\n%s\n" "${delimiter}"
@@ -222,7 +223,7 @@ fi
 # Try using TCMalloc on Linux
 prepare_tcmalloc() {
     if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
-        TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
+        TCMALLOC="$(PATH=/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
         if [[ ! -z "${TCMALLOC}" ]]; then
             echo "Using TCMalloc: ${TCMALLOC}"
             export LD_PRELOAD="${TCMALLOC}"