Merge branch 'AUTOMATIC1111:master' into img2img-api-scripts

24 files changed, 823 insertions, 275 deletions

diff --git a/README.md b/README.md
index 88250a6b..d783fdf0 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,7 @@
 # Stable Diffusion web UI

 A browser interface based on Gradio library for Stable Diffusion.

 

-![](txt2img_Screenshot.png)

-

-Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users.

+![](screenshot.png)

 

 ## Features

 [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):

@@ -97,9 +95,8 @@ Alternatively, use online services (like Google Colab):
 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"

 2. Install [git](https://git-scm.com/download/win).

 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.

-4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).

-5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).

-6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.

+4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).

+5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.

 

 ### Automatic Installation on Linux

 1. Install the dependencies:

@@ -141,6 +138,7 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion

 - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.

 - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)

+- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)

 - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).

 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd

 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot

diff --git a/html/licenses.html b/html/licenses.html
index 9eeaa072..570630eb 100644
--- a/html/licenses.html
+++ b/html/licenses.html
@@ -184,7 +184,7 @@ SOFTWARE.
 </pre>

 

 <h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>

-<small>Code added by contirubtors, most likely copied from this repository.</small>

+<small>Code added by contributors, most likely copied from this repository.</small>

 

 <pre>

                                  Apache License

@@ -390,3 +390,30 @@ SOFTWARE.
    limitations under the License.

 </pre>

 

+<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>

+<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>

+<pre>

+MIT License

+

+Copyright (c) 2023 Alex Birch

+Copyright (c) 2023 Amin Rezaei

+

+Permission is hereby granted, free of charge, to any person obtaining a copy

+of this software and associated documentation files (the "Software"), to deal

+in the Software without restriction, including without limitation the rights

+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

+copies of the Software, and to permit persons to whom the Software is

+furnished to do so, subject to the following conditions:

+

+The above copyright notice and this permission notice shall be included in all

+copies or substantial portions of the Software.

+

+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

+SOFTWARE.

+</pre>

+

diff --git a/modules/api/models.py b/modules/api/models.py
index 862477e7..c85eb94d 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -125,7 +125,7 @@ class ExtrasBaseRequest(BaseModel):
     gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
     codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
     codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
-    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
     upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
     upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
diff --git a/modules/devices.py b/modules/devices.py
index 800510b7..caeb0276 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -133,8 +133,26 @@ def numpy_fix(self, *args, **kwargs):
     return orig_tensor_numpy(self, *args, **kwargs)
 
 
-# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
-if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
-    torch.Tensor.to = tensor_to_fix
-    torch.nn.functional.layer_norm = layer_norm_fix
-    torch.Tensor.numpy = numpy_fix
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+orig_cumsum = torch.cumsum
+orig_Tensor_cumsum = torch.Tensor.cumsum
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+    if input.device.type == 'mps':
+        output_dtype = kwargs.get('dtype', input.dtype)
+        if any(output_dtype == broken_dtype for broken_dtype in [torch.bool, torch.int8, torch.int16, torch.int64]):
+            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
+    return cumsum_func(input, *args, **kwargs)
+
+
+if has_mps():
+    if version.parse(torch.__version__) < version.parse("1.13"):
+        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+        torch.Tensor.to = tensor_to_fix
+        torch.nn.functional.layer_norm = layer_norm_fix
+        torch.Tensor.numpy = numpy_fix
+    elif version.parse(torch.__version__) > version.parse("1.13.1"):
+        if not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.Tensor([1,1]).to(torch.device("mps")).cumsum(0, dtype=torch.int16)):
+            torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
+            torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
+        orig_narrow = torch.narrow
+        torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 6a9b1398..b0cfbe71 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -13,7 +13,7 @@ import tqdm
 from einops import rearrange, repeat

 from ldm.util import default

 from modules import devices, processing, sd_models, shared, sd_samplers

-from modules.textual_inversion import textual_inversion

+from modules.textual_inversion import textual_inversion, logging

 from modules.textual_inversion.learn_schedule import LearnRateScheduler

 from torch import einsum

 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_

@@ -457,7 +457,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     pin_memory = shared.opts.pin_memory

 

     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

-    

+

+    if shared.opts.save_training_settings_to_txt:

+        saved_params = dict(

+            model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds),

+            **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}

+        )

+        logging.save_settings_to_file(log_directory, {**saved_params, **locals()})

+

     latent_sampling_method = ds.latent_sampling_method

 

     dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)

diff --git a/modules/processing.py b/modules/processing.py
index d5ac7eb1..1d23b15f 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -711,7 +711,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.truncate_x = 0

         self.truncate_y = 0

 

-

     def init(self, all_prompts, all_seeds, all_subseeds):

         if self.enable_hr:

             if self.hr_resize_x == 0 and self.hr_resize_y == 0:

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index de69fd9f..608c5300 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -71,6 +71,7 @@ callback_map = dict(
     callbacks_before_component=[],

     callbacks_after_component=[],

     callbacks_image_grid=[],

+    callbacks_script_unloaded=[],

 )

 

 

@@ -171,6 +172,14 @@ def image_grid_callback(params: ImageGridLoopParams):
             report_exception(c, 'image_grid')

 

 

+def script_unloaded_callback():

+    for c in reversed(callback_map['callbacks_script_unloaded']):

+        try:

+            c.callback()

+        except Exception:

+            report_exception(c, 'script_unloaded')

+

+

 def add_callback(callbacks, fun):

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

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

@@ -202,7 +211,7 @@ def on_app_started(callback):
 

 def on_model_loaded(callback):

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

-    passed as an argument"""

+    passed as an argument; this function is also called when the script is reloaded. """

     add_callback(callback_map['callbacks_model_loaded'], callback)

 

 

@@ -279,3 +288,10 @@ def on_image_grid(callback):
        - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.

     """

     add_callback(callback_map['callbacks_image_grid'], callback)

+

+

+def on_script_unloaded(callback):

+    """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that

+    the script did should be reverted here"""

+

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

diff --git a/modules/scripts.py b/modules/scripts.py
index 0c44f191..35164093 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -290,7 +290,6 @@ class ScriptRunner:
             script.group = group

 

         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")

-        dropdown.save_to_config = True

         inputs[0] = dropdown

 

         for script in self.selectable_scripts:

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index fa2cd4bb..cfdb09d6 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -7,8 +7,6 @@ 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.sd_hijack_optimizations import invokeAI_mps_available

-

 import ldm.modules.attention

 import ldm.modules.diffusionmodules.model

 import ldm.modules.diffusionmodules.openaimodel

@@ -43,20 +41,19 @@ def apply_optimizations():
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward

         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward

         optimization_method = 'xformers'

+    elif cmd_opts.opt_sub_quad_attention:

+        print("Applying sub-quadratic cross attention optimization.")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward

+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward

+        optimization_method = 'sub-quadratic'

     elif cmd_opts.opt_split_attention_v1:

         print("Applying v1 cross attention optimization.")

         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

         optimization_method = 'V1'

-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):

-        if not invokeAI_mps_available and shared.device.type == 'mps':

-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")

-            print("Applying v1 cross attention optimization.")

-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1

-            optimization_method = 'V1'

-        else:

-            print("Applying cross attention optimization (InvokeAI).")

-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI

-            optimization_method = 'InvokeAI'

+    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):

+        print("Applying cross attention optimization (InvokeAI).")

+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI

+        optimization_method = 'InvokeAI'

     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):

         print("Applying cross attention optimization (Doggettx).")

         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward

@@ -150,10 +147,10 @@ class StableDiffusionModelHijack:
     def clear_comments(self):

         self.comments = []

 

-    def tokenize(self, text):

-        _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])

+    def get_prompt_lengths(self, text):

+        _, token_count = self.clip.process_texts([text])

 

-        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)

+        return token_count, self.clip.get_target_prompt_token_count(token_count)

 

 

 class EmbeddingsWithFixes(torch.nn.Module):

diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index ca92b142..5520c9b2 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -1,30 +1,89 @@
 import math

+from collections import namedtuple

 

 import torch

 

-from modules import prompt_parser, devices

+from modules import prompt_parser, devices, sd_hijack

 from modules.shared import opts

 

-def get_target_prompt_token_count(token_count):

-    return math.ceil(max(token_count, 1) / 75) * 75

+

+class PromptChunk:

+    """

+    This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.

+    If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.

+    Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,

+    so just 75 tokens from prompt.

+    """

+

+    def __init__(self):

+        self.tokens = []

+        self.multipliers = []

+        self.fixes = []

+

+

+PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])

+"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt

+chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally

+are applied by sd_hijack.EmbeddingsWithFixes's forward function."""

 

 

 class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):

+    """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to

+    have unlimited prompt length and assign weights to tokens in prompt.

+    """

+

     def __init__(self, wrapped, hijack):

         super().__init__()

+

         self.wrapped = wrapped

-        self.hijack = hijack

+        """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,

+        depending on model."""

+

+        self.hijack: sd_hijack.StableDiffusionModelHijack = hijack

+        self.chunk_length = 75

+

+    def empty_chunk(self):

+        """creates an empty PromptChunk and returns it"""

+

+        chunk = PromptChunk()

+        chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)

+        chunk.multipliers = [1.0] * (self.chunk_length + 2)

+        return chunk

+

+    def get_target_prompt_token_count(self, token_count):

+        """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""

+

+        return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length

 

     def tokenize(self, texts):

+        """Converts a batch of texts into a batch of token ids"""

+

         raise NotImplementedError

 

     def encode_with_transformers(self, tokens):

+        """

+        converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;

+        All python lists with tokens are assumed to have same length, usually 77.

+        if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on

+        model - can be 768 and 1024.

+        Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).

+        """

+

         raise NotImplementedError

 

     def encode_embedding_init_text(self, init_text, nvpt):

+        """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through

+        transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""

+

         raise NotImplementedError

 

-    def tokenize_line(self, line, used_custom_terms, hijack_comments):

+    def tokenize_line(self, line):

+        """

+        this transforms a single prompt into a list of PromptChunk objects - as many as needed to

+        represent the prompt.

+        Returns the list and the total number of tokens in the prompt.

+        """

+

         if opts.enable_emphasis:

             parsed = prompt_parser.parse_prompt_attention(line)

         else:

@@ -32,205 +91,152 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 

         tokenized = self.tokenize([text for text, _ in parsed])

 

-        fixes = []

-        remade_tokens = []

-        multipliers = []

+        chunks = []

+        chunk = PromptChunk()

+        token_count = 0

         last_comma = -1

 

-        for tokens, (text, weight) in zip(tokenized, parsed):

-            i = 0

-            while i < len(tokens):

-                token = tokens[i]

+        def next_chunk():

+            """puts current chunk into the list of results and produces the next one - empty"""

+            nonlocal token_count

+            nonlocal last_comma

+            nonlocal chunk

 

-                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

+            token_count += len(chunk.tokens)

+            to_add = self.chunk_length - len(chunk.tokens)

+            if to_add > 0:

+                chunk.tokens += [self.id_end] * to_add

+                chunk.multipliers += [1.0] * to_add

+

+            chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]

+            chunk.multipliers = [1.0] + chunk.multipliers + [1.0]

+

+            last_comma = -1

+            chunks.append(chunk)

+            chunk = PromptChunk()

+

+        for tokens, (text, weight) in zip(tokenized, parsed):

+            position = 0

+            while position < len(tokens):

+                token = tokens[position]

 

                 if token == self.comma_token:

-                    last_comma = len(remade_tokens)

-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:

-                    last_comma += 1

-                    reloc_tokens = remade_tokens[last_comma:]

-                    reloc_mults = multipliers[last_comma:]

+                    last_comma = len(chunk.tokens)

+

+                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack

+                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.

+                elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:

+                    break_location = last_comma + 1

 

-                    remade_tokens = remade_tokens[:last_comma]

-                    length = len(remade_tokens)

+                    reloc_tokens = chunk.tokens[break_location:]

+                    reloc_mults = chunk.multipliers[break_location:]

 

-                    rem = int(math.ceil(length / 75)) * 75 - length

-                    remade_tokens += [self.id_end] * rem + reloc_tokens

-                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults

+                    chunk.tokens = chunk.tokens[:break_location]

+                    chunk.multipliers = chunk.multipliers[:break_location]

 

+                    next_chunk()

+                    chunk.tokens = reloc_tokens

+                    chunk.multipliers = reloc_mults

+

+                if len(chunk.tokens) == self.chunk_length:

+                    next_chunk()

+

+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)

                 if embedding is None:

-                    remade_tokens.append(token)

-                    multipliers.append(weight)

-                    i += 1

-                else:

-                    emb_len = int(embedding.vec.shape[0])

-                    iteration = len(remade_tokens) // 75

-                    if (len(remade_tokens) + emb_len) // 75 != iteration:

-                        rem = (75 * (iteration + 1) - len(remade_tokens))

-                        remade_tokens += [self.id_end] * rem

-                        multipliers += [1.0] * rem

-                        iteration += 1

-                    fixes.append((iteration, (len(remade_tokens) % 75, embedding)))

-                    remade_tokens += [0] * emb_len

-                    multipliers += [weight] * emb_len

-                    used_custom_terms.append((embedding.name, embedding.checksum()))

-                    i += embedding_length_in_tokens

-

-        token_count = len(remade_tokens)

-        prompt_target_length = get_target_prompt_token_count(token_count)

-        tokens_to_add = prompt_target_length - len(remade_tokens)

-

-        remade_tokens = remade_tokens + [self.id_end] * tokens_to_add

-        multipliers = multipliers + [1.0] * tokens_to_add

-

-        return remade_tokens, fixes, multipliers, token_count

-

-    def process_text(self, texts):

-        used_custom_terms = []

-        remade_batch_tokens = []

-        hijack_comments = []

-        hijack_fixes = []

+                    chunk.tokens.append(token)

+                    chunk.multipliers.append(weight)

+                    position += 1

+                    continue

+

+                emb_len = int(embedding.vec.shape[0])

+                if len(chunk.tokens) + emb_len > self.chunk_length:

+                    next_chunk()

+

+                chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))

+

+                chunk.tokens += [0] * emb_len

+                chunk.multipliers += [weight] * emb_len

+                position += embedding_length_in_tokens

+

+        if len(chunk.tokens) > 0 or len(chunks) == 0:

+            next_chunk()

+

+        return chunks, token_count

+

+    def process_texts(self, texts):

+        """

+        Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum

+        length, in tokens, of all texts.

+        """

+

         token_count = 0

 

         cache = {}

-        batch_multipliers = []

+        batch_chunks = []

         for line in texts:

             if line in cache:

-                remade_tokens, fixes, multipliers = cache[line]

+                chunks = cache[line]

             else:

-                remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)

+                chunks, current_token_count = self.tokenize_line(line)

                 token_count = max(current_token_count, token_count)

 

-                cache[line] = (remade_tokens, fixes, multipliers)

+                cache[line] = chunks

 

-            remade_batch_tokens.append(remade_tokens)

-            hijack_fixes.append(fixes)

-            batch_multipliers.append(multipliers)

+            batch_chunks.append(chunks)

 

-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

+        return batch_chunks, token_count

 

-    def process_text_old(self, texts):

-        id_start = self.id_start

-        id_end = self.id_end

-        maxlen = self.wrapped.max_length  # you get to stay at 77

-        used_custom_terms = []

-        remade_batch_tokens = []

-        hijack_comments = []

-        hijack_fixes = []

-        token_count = 0

+    def forward(self, texts):

+        """

+        Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.

+        Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will

+        be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024.

+        An example shape returned by this function can be: (2, 77, 768).

+        Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet

+        is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"

+        """

 

-        cache = {}

-        batch_tokens = self.tokenize(texts)

-        batch_multipliers = []

-        for tokens in batch_tokens:

-            tuple_tokens = tuple(tokens)

+        if opts.use_old_emphasis_implementation:

+            import modules.sd_hijack_clip_old

+            return modules.sd_hijack_clip_old.forward_old(self, texts)

 

-            if tuple_tokens in cache:

-                remade_tokens, fixes, multipliers = cache[tuple_tokens]

-            else:

-                fixes = []

-                remade_tokens = []

-                multipliers = []

-                mult = 1.0

-

-                i = 0

-                while i < len(tokens):

-                    token = tokens[i]

-

-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

-

-                    mult_change = self.token_mults.get(token) if opts.enable_emphasis else None

-                    if mult_change is not None:

-                        mult *= mult_change

-                        i += 1

-                    elif embedding is None:

-                        remade_tokens.append(token)

-                        multipliers.append(mult)

-                        i += 1

-                    else:

-                        emb_len = int(embedding.vec.shape[0])

-                        fixes.append((len(remade_tokens), embedding))

-                        remade_tokens += [0] * emb_len

-                        multipliers += [mult] * emb_len

-                        used_custom_terms.append((embedding.name, embedding.checksum()))

-                        i += embedding_length_in_tokens

-

-                if len(remade_tokens) > maxlen - 2:

-                    vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}

-                    ovf = remade_tokens[maxlen - 2:]

-                    overflowing_words = [vocab.get(int(x), "") for x in ovf]

-                    overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))

-                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")

-

-                token_count = len(remade_tokens)

-                remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))

-                remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]

-                cache[tuple_tokens] = (remade_tokens, fixes, multipliers)

-

-            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))

-            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]

-

-            remade_batch_tokens.append(remade_tokens)

-            hijack_fixes.append(fixes)

-            batch_multipliers.append(multipliers)

-        return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

-

-    def forward(self, text):

-        use_old = opts.use_old_emphasis_implementation

-        if use_old:

-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)

-        else:

-            batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)

-

-        self.hijack.comments += hijack_comments

-

-        if len(used_custom_terms) > 0:

-            self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))

-

-        if use_old:

-            self.hijack.fixes = hijack_fixes

-            return self.process_tokens(remade_batch_tokens, batch_multipliers)

-

-        z = None

-        i = 0

-        while max(map(len, remade_batch_tokens)) != 0:

-            rem_tokens = [x[75:] for x in remade_batch_tokens]

-            rem_multipliers = [x[75:] for x in batch_multipliers]

-

-            self.hijack.fixes = []

-            for unfiltered in hijack_fixes:

-                fixes = []

-                for fix in unfiltered:

-                    if fix[0] == i:

-                        fixes.append(fix[1])

-                self.hijack.fixes.append(fixes)

-

-            tokens = []

-            multipliers = []

-            for j in range(len(remade_batch_tokens)):

-                if len(remade_batch_tokens[j]) > 0:

-                    tokens.append(remade_batch_tokens[j][:75])

-                    multipliers.append(batch_multipliers[j][:75])

-                else:

-                    tokens.append([self.id_end] * 75)

-                    multipliers.append([1.0] * 75)

-

-            z1 = self.process_tokens(tokens, multipliers)

-            z = z1 if z is None else torch.cat((z, z1), axis=-2)

-

-            remade_batch_tokens = rem_tokens

-            batch_multipliers = rem_multipliers

-            i += 1

+        batch_chunks, token_count = self.process_texts(texts)

 

-        return z

+        used_embeddings = {}

+        chunk_count = max([len(x) for x in batch_chunks])

 

-    def process_tokens(self, remade_batch_tokens, batch_multipliers):

-        if not opts.use_old_emphasis_implementation:

-            remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]

-            batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]

+        zs = []

+        for i in range(chunk_count):

+            batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]

+

+            tokens = [x.tokens for x in batch_chunk]

+            multipliers = [x.multipliers for x in batch_chunk]

+            self.hijack.fixes = [x.fixes for x in batch_chunk]

 

+            for fixes in self.hijack.fixes:

+                for position, embedding in fixes:

+                    used_embeddings[embedding.name] = embedding

+

+            z = self.process_tokens(tokens, multipliers)

+            zs.append(z)

+

+        if len(used_embeddings) > 0:

+            embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()])

+            self.hijack.comments.append(f"Used embeddings: {embeddings_list}")

+

+        return torch.hstack(zs)

+

+    def process_tokens(self, remade_batch_tokens, batch_multipliers):

+        """

+        sends one single prompt chunk to be encoded by transformers neural network.

+        remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually

+        there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.

+        Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier

+        corresponds to one token.

+        """

         tokens = torch.asarray(remade_batch_tokens).to(devices.device)

 

+        # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.

         if self.id_end != self.id_pad:

             for batch_pos in range(len(remade_batch_tokens)):

                 index = remade_batch_tokens[batch_pos].index(self.id_end)

@@ -239,8 +245,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         z = self.encode_with_transformers(tokens)

 

         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise

-        batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]

-        batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)

+        batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)

         original_mean = z.mean()

         z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)

         new_mean = z.mean()

diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py
new file mode 100644
index 00000000..6d9fbbe6
--- /dev/null
+++ b/modules/sd_hijack_clip_old.py
@@ -0,0 +1,81 @@
+from modules import sd_hijack_clip

+from modules import shared

+

+

+def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):

+    id_start = self.id_start

+    id_end = self.id_end

+    maxlen = self.wrapped.max_length  # you get to stay at 77

+    used_custom_terms = []

+    remade_batch_tokens = []

+    hijack_comments = []

+    hijack_fixes = []

+    token_count = 0

+

+    cache = {}

+    batch_tokens = self.tokenize(texts)

+    batch_multipliers = []

+    for tokens in batch_tokens:

+        tuple_tokens = tuple(tokens)

+

+        if tuple_tokens in cache:

+            remade_tokens, fixes, multipliers = cache[tuple_tokens]

+        else:

+            fixes = []

+            remade_tokens = []

+            multipliers = []

+            mult = 1.0

+

+            i = 0

+            while i < len(tokens):

+                token = tokens[i]

+

+                embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)

+

+                mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None

+                if mult_change is not None:

+                    mult *= mult_change

+                    i += 1

+                elif embedding is None:

+                    remade_tokens.append(token)

+                    multipliers.append(mult)

+                    i += 1

+                else:

+                    emb_len = int(embedding.vec.shape[0])

+                    fixes.append((len(remade_tokens), embedding))

+                    remade_tokens += [0] * emb_len

+                    multipliers += [mult] * emb_len

+                    used_custom_terms.append((embedding.name, embedding.checksum()))

+                    i += embedding_length_in_tokens

+

+            if len(remade_tokens) > maxlen - 2:

+                vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}

+                ovf = remade_tokens[maxlen - 2:]

+                overflowing_words = [vocab.get(int(x), "") for x in ovf]

+                overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))

+                hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")

+

+            token_count = len(remade_tokens)

+            remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))

+            remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]

+            cache[tuple_tokens] = (remade_tokens, fixes, multipliers)

+

+        multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))

+        multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]

+

+        remade_batch_tokens.append(remade_tokens)

+        hijack_fixes.append(fixes)

+        batch_multipliers.append(multipliers)

+    return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count

+

+

+def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):

+    batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)

+

+    self.hijack.comments += hijack_comments

+

+    if len(used_custom_terms) > 0:

+        self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))

+

+    self.hijack.fixes = hijack_fixes

+    return self.process_tokens(remade_batch_tokens, batch_multipliers)

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 02c87f40..cdc63ed7 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,7 @@
 import math

 import sys

 import traceback

-import importlib

+import psutil

 

 import torch

 from torch import einsum

@@ -12,6 +12,8 @@ from einops import rearrange
 from modules import shared

 from modules.hypernetworks import hypernetwork

 

+from .sub_quadratic_attention import efficient_dot_product_attention

+

 

 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:

     try:

@@ -22,6 +24,19 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
         print(traceback.format_exc(), file=sys.stderr)

 

 

+def get_available_vram():

+    if shared.device.type == 'cuda':

+        stats = torch.cuda.memory_stats(shared.device)

+        mem_active = stats['active_bytes.all.current']

+        mem_reserved = stats['reserved_bytes.all.current']

+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

+        mem_free_torch = mem_reserved - mem_active

+        mem_free_total = mem_free_cuda + mem_free_torch

+        return mem_free_total

+    else:

+        return psutil.virtual_memory().available

+

+

 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion

 def split_cross_attention_forward_v1(self, x, context=None, mask=None):

     h = self.heads

@@ -76,12 +91,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 

     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)

 

-    stats = torch.cuda.memory_stats(q.device)

-    mem_active = stats['active_bytes.all.current']

-    mem_reserved = stats['reserved_bytes.all.current']

-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

-    mem_free_torch = mem_reserved - mem_active

-    mem_free_total = mem_free_cuda + mem_free_torch

+    mem_free_total = get_available_vram()

 

     gb = 1024 ** 3

     tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()

@@ -118,19 +128,8 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     return self.to_out(r2)

 

 

-def check_for_psutil():

-    try:

-        spec = importlib.util.find_spec('psutil')

-        return spec is not None

-    except ModuleNotFoundError:

-        return False

-

-invokeAI_mps_available = check_for_psutil()

-

 # -- Taken from https://github.com/invoke-ai/InvokeAI and modified --

-if invokeAI_mps_available:

-    import psutil

-    mem_total_gb = psutil.virtual_memory().total // (1 << 30)

+mem_total_gb = psutil.virtual_memory().total // (1 << 30)

 

 def einsum_op_compvis(q, k, v):

     s = einsum('b i d, b j d -> b i j', q, k)

@@ -215,6 +214,71 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
 

 # -- End of code from https://github.com/invoke-ai/InvokeAI --

 

+

+# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1

+# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface

+def sub_quad_attention_forward(self, x, context=None, mask=None):

+    assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."

+

+    h = self.heads

+

+    q = self.to_q(x)

+    context = default(context, x)

+

+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)

+    k = self.to_k(context_k)

+    v = self.to_v(context_v)

+    del context, context_k, context_v, x

+

+    q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)

+    k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)

+    v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)

+

+    x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)

+

+    x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)

+

+    out_proj, dropout = self.to_out

+    x = out_proj(x)

+    x = dropout(x)

+

+    return x

+

+def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):

+    bytes_per_token = torch.finfo(q.dtype).bits//8

+    batch_x_heads, q_tokens, _ = q.shape

+    _, k_tokens, _ = k.shape

+    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens

+

+    if chunk_threshold is None:

+        chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)

+    elif chunk_threshold == 0:

+        chunk_threshold_bytes = None

+    else:

+        chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())

+

+    if kv_chunk_size_min is None and chunk_threshold_bytes is not None:

+        kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))

+    elif kv_chunk_size_min == 0:

+        kv_chunk_size_min = None

+

+    if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:

+        # the big matmul fits into our memory limit; do everything in 1 chunk,

+        # i.e. send it down the unchunked fast-path

+        query_chunk_size = q_tokens

+        kv_chunk_size = k_tokens

+

+    return efficient_dot_product_attention(

+        q,

+        k,

+        v,

+        query_chunk_size=q_chunk_size,

+        kv_chunk_size=kv_chunk_size,

+        kv_chunk_size_min = kv_chunk_size_min,

+        use_checkpoint=use_checkpoint,

+    )

+

+

 def xformers_attention_forward(self, x, context=None, mask=None):

     h = self.heads

     q_in = self.to_q(x)

@@ -252,12 +316,7 @@ def cross_attention_attnblock_forward(self, x):
 

         h_ = torch.zeros_like(k, device=q.device)

 

-        stats = torch.cuda.memory_stats(q.device)

-        mem_active = stats['active_bytes.all.current']

-        mem_reserved = stats['reserved_bytes.all.current']

-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())

-        mem_free_torch = mem_reserved - mem_active

-        mem_free_total = mem_free_cuda + mem_free_torch

+        mem_free_total = get_available_vram()

 

         tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()

         mem_required = tensor_size * 2.5

@@ -312,3 +371,19 @@ def xformers_attnblock_forward(self, x):
         return x + out

     except NotImplementedError:

         return cross_attention_attnblock_forward(self, x)

+

+def sub_quad_attnblock_forward(self, x):

+    h_ = x

+    h_ = self.norm(h_)

+    q = self.q(h_)

+    k = self.k(h_)

+    v = self.v(h_)

+    b, c, h, w = q.shape

+    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))

+    q = q.contiguous()

+    k = k.contiguous()

+    v = v.contiguous()

+    out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)

+    out = rearrange(out, 'b (h w) c -> b c h w', h=h)

+    out = self.proj_out(out)

+    return x + out

diff --git a/modules/shared.py b/modules/shared.py
index e0f44c6d..a6712dae 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -56,6 +56,10 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")

 parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")

 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")

+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")

+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)

+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)

+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)

 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")

 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")

@@ -362,6 +366,7 @@ options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),

     "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."),

+    "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),

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

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

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

@@ -429,7 +434,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),

     "dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"),

     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),

-    'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"),

+    'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),

     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),

 }))

 

@@ -576,6 +581,7 @@ latent_upscale_modes = {
     "Latent (bicubic)": {"mode": "bicubic", "antialias": False},

     "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},

     "Latent (nearest)": {"mode": "nearest", "antialias": False},

+    "Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},

 }

 

 sd_upscalers = []

diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
new file mode 100644
index 00000000..fea7aaac
--- /dev/null
+++ b/modules/sub_quadratic_attention.py
@@ -0,0 +1,205 @@
+# original source:
+#   https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
+# license:
+#   MIT License (see Memory Efficient Attention under the Licenses section in the web UI interface for the full license)
+# credit:
+#   Amin Rezaei (original author)
+#   Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
+#   brkirch (modified to use torch.narrow instead of dynamic_slice implementation)
+# implementation of:
+#   Self-attention Does Not Need O(n2) Memory":
+#   https://arxiv.org/abs/2112.05682v2
+
+from functools import partial
+import torch
+from torch import Tensor
+from torch.utils.checkpoint import checkpoint
+import math
+from typing import Optional, NamedTuple, Protocol, List
+
+def narrow_trunc(
+    input: Tensor,
+    dim: int,
+    start: int,
+    length: int
+) -> Tensor:
+    return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
+
+class AttnChunk(NamedTuple):
+    exp_values: Tensor
+    exp_weights_sum: Tensor
+    max_score: Tensor
+
+class SummarizeChunk(Protocol):
+    @staticmethod
+    def __call__(
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+    ) -> AttnChunk: ...
+
+class ComputeQueryChunkAttn(Protocol):
+    @staticmethod
+    def __call__(
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+    ) -> Tensor: ...
+
+def _summarize_chunk(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    scale: float,
+) -> AttnChunk:
+    attn_weights = torch.baddbmm(
+        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        query,
+        key.transpose(1,2),
+        alpha=scale,
+        beta=0,
+    )
+    max_score, _ = torch.max(attn_weights, -1, keepdim=True)
+    max_score = max_score.detach()
+    exp_weights = torch.exp(attn_weights - max_score)
+    exp_values = torch.bmm(exp_weights, value)
+    max_score = max_score.squeeze(-1)
+    return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
+
+def _query_chunk_attention(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    summarize_chunk: SummarizeChunk,
+    kv_chunk_size: int,
+) -> Tensor:
+    batch_x_heads, k_tokens, k_channels_per_head = key.shape
+    _, _, v_channels_per_head = value.shape
+
+    def chunk_scanner(chunk_idx: int) -> AttnChunk:
+        key_chunk = narrow_trunc(
+            key,
+            1,
+            chunk_idx,
+            kv_chunk_size
+        )
+        value_chunk = narrow_trunc(
+            value,
+            1,
+            chunk_idx,
+            kv_chunk_size
+        )
+        return summarize_chunk(query, key_chunk, value_chunk)
+
+    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)))
+    chunk_values, chunk_weights, chunk_max = acc_chunk
+
+    global_max, _ = torch.max(chunk_max, 0, keepdim=True)
+    max_diffs = torch.exp(chunk_max - global_max)
+    chunk_values *= torch.unsqueeze(max_diffs, -1)
+    chunk_weights *= max_diffs
+
+    all_values = chunk_values.sum(dim=0)
+    all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
+    return all_values / all_weights
+
+# TODO: refactor CrossAttention#get_attention_scores to share code with this
+def _get_attention_scores_no_kv_chunking(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    scale: float,
+) -> Tensor:
+    attn_scores = torch.baddbmm(
+        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        query,
+        key.transpose(1,2),
+        alpha=scale,
+        beta=0,
+    )
+    attn_probs = attn_scores.softmax(dim=-1)
+    del attn_scores
+    hidden_states_slice = torch.bmm(attn_probs, value)
+    return hidden_states_slice
+
+class ScannedChunk(NamedTuple):
+    chunk_idx: int
+    attn_chunk: AttnChunk
+
+def efficient_dot_product_attention(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    query_chunk_size=1024,
+    kv_chunk_size: Optional[int] = None,
+    kv_chunk_size_min: Optional[int] = None,
+    use_checkpoint=True,
+):
+    """Computes efficient dot-product attention given query, key, and value.
+      This is efficient version of attention presented in
+      https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
+      Args:
+        query: queries for calculating attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        key: keys for calculating attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        value: values to be used in attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        query_chunk_size: int: query chunks size
+        kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
+        kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
+        use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
+      Returns:
+        Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
+      """
+    batch_x_heads, q_tokens, q_channels_per_head = query.shape
+    _, k_tokens, _ = key.shape
+    scale = q_channels_per_head ** -0.5
+
+    kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
+    if kv_chunk_size_min is not None:
+        kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
+
+    def get_query_chunk(chunk_idx: int) -> Tensor:
+        return narrow_trunc(
+            query,
+            1,
+            chunk_idx,
+            min(query_chunk_size, q_tokens)
+        )
+    
+    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
+    summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
+    compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
+        _get_attention_scores_no_kv_chunking,
+        scale=scale
+    ) if k_tokens <= kv_chunk_size else (
+        # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
+        partial(
+            _query_chunk_attention,
+            kv_chunk_size=kv_chunk_size,
+            summarize_chunk=summarize_chunk,
+        )
+    )
+
+    if q_tokens <= query_chunk_size:
+        # fast-path for when there's just 1 query chunk
+        return compute_query_chunk_attn(
+            query=query,
+            key=key,
+            value=value,
+        )
+    
+    # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
+    # and pass slices to be mutated, instead of torch.cat()ing the returned slices
+    res = torch.cat([
+        compute_query_chunk_attn(
+            query=get_query_chunk(i * query_chunk_size),
+            key=key,
+            value=value,
+        ) for i in range(math.ceil(q_tokens / query_chunk_size))
+    ], dim=1)
+    return res
diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py
new file mode 100644
index 00000000..8b1981d5
--- /dev/null
+++ b/modules/textual_inversion/logging.py
@@ -0,0 +1,24 @@
+import datetime

+import json

+import os

+

+saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file"}

+saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"}

+saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"}

+saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet

+saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"}

+

+

+def save_settings_to_file(log_directory, all_params):

+    now = datetime.datetime.now()

+    params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}

+

+    keys = saved_params_all

+    if all_params.get('preview_from_txt2img'):

+        keys = keys | saved_params_previews

+

+    params.update({k: v for k, v in all_params.items() if k in keys})

+

+    filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json'

+    with open(os.path.join(log_directory, filename), "w") as file:

+        json.dump(params, file, indent=4)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 24b43045..45882ed6 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,6 +1,7 @@
 import os

 import sys

 import traceback

+import inspect

 

 import torch

 import tqdm

@@ -17,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,

                                                        insert_image_data_embed, extract_image_data_embed,

                                                        caption_image_overlay)

+from modules.textual_inversion.logging import save_settings_to_file

+

 

 class Embedding:

     def __init__(self, vec, name, step=None):

@@ -76,7 +79,6 @@ class EmbeddingDatabase:
 

         self.word_embeddings[embedding.name] = embedding

 

-        # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working

         ids = model.cond_stage_model.tokenize([embedding.name])[0]

 

         first_id = ids[0]

@@ -149,18 +151,19 @@ class EmbeddingDatabase:
             else:

                 self.skipped_embeddings[name] = embedding

 

-        for fn in os.listdir(self.embeddings_dir):

-            try:

-                fullfn = os.path.join(self.embeddings_dir, fn)

+        for root, dirs, fns in os.walk(self.embeddings_dir):

+            for fn in fns:

+                try:

+                    fullfn = os.path.join(root, fn)

 

-                if os.stat(fullfn).st_size == 0:

-                    continue

+                    if os.stat(fullfn).st_size == 0:

+                        continue

 

-                process_file(fullfn, fn)

-            except Exception:

-                print(f"Error loading embedding {fn}:", file=sys.stderr)

-                print(traceback.format_exc(), file=sys.stderr)

-                continue

+                    process_file(fullfn, fn)

+                except Exception:

+                    print(f"Error loading embedding {fn}:", file=sys.stderr)

+                    print(traceback.format_exc(), file=sys.stderr)

+                    continue

 

         print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")

         if len(self.skipped_embeddings) > 0:

@@ -229,6 +232,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,

         })

 

+

 def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):

     assert model_name, f"{name} not selected"

     assert learn_rate, "Learning rate is empty or 0"

@@ -292,8 +296,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     if initial_step >= steps:

         shared.state.textinfo = "Model has already been trained beyond specified max steps"

         return embedding, filename

+    

     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

-

     clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \

         torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \

         None

@@ -307,6 +311,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 

     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

 

+    if shared.opts.save_training_settings_to_txt:

+        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})

+

     latent_sampling_method = ds.latent_sampling_method

 

     dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)

diff --git a/modules/ui.py b/modules/ui.py
index 81d96c5b..99483130 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -20,7 +20,7 @@ from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call

 

 from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru

-from modules.ui_components import FormRow, FormGroup, ToolButton

+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML

 from modules.paths import script_path

 

 from modules.shared import opts, cmd_opts, restricted_opts

@@ -256,6 +256,20 @@ def add_style(name: str, prompt: str, negative_prompt: str):
     return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]

 

 

+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):

+    from modules import processing, devices

+

+    if not enable:

+        return ""

+

+    p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)

+

+    with devices.autocast():

+        p.init([""], [0], [0])

+

+    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{p.hr_upscale_to_x}x{p.hr_upscale_to_y}</span>"

+

+

 def apply_styles(prompt, prompt_neg, style1_name, style2_name):

     prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])

     prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])

@@ -368,7 +382,7 @@ def update_token_counter(text, steps):
 

     flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)

     prompts = [prompt_text for step, prompt_text in flat_prompts]

-    tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])

+    token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])

     style_class = ' class="red"' if (token_count > max_length) else ""

     return f"<span {style_class}>{token_count}/{max_length}</span>"

 

@@ -435,11 +449,9 @@ def create_toprow(is_img2img):
             with gr.Row():

                 with gr.Column(scale=1, elem_id="style_pos_col"):

                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))

-                    prompt_style.save_to_config = True

 

                 with gr.Column(scale=1, elem_id="style_neg_col"):

                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))

-                    prompt_style2.save_to_config = True

 

     return prompt, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button

 

@@ -550,6 +562,8 @@ Requested path was: {f}
                 os.startfile(path)

             elif platform.system() == "Darwin":

                 sp.Popen(["open", path])

+            elif "microsoft-standard-WSL2" in platform.uname().release:

+                sp.Popen(["wsl-open", path])

             else:

                 sp.Popen(["xdg-open", path])

 

@@ -636,7 +650,6 @@ def create_sampler_and_steps_selection(choices, tabname):
     if opts.samplers_in_dropdown:

         with FormRow(elem_id=f"sampler_selection_{tabname}"):

             sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")

-            sampler_index.save_to_config = True

             steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)

     else:

         with FormGroup(elem_id=f"sampler_selection_{tabname}"):

@@ -707,6 +720,7 @@ def create_ui():
                             restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")

                             tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")

                             enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")

+                            hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)

 

                     elif category == "hires_fix":

                         with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:

@@ -730,6 +744,17 @@ def create_ui():
                         with FormGroup(elem_id="txt2img_script_container"):

                             custom_inputs = modules.scripts.scripts_txt2img.setup_ui()

 

+            hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]

+            hr_resolution_preview_args = dict(

+                fn=calc_resolution_hires,

+                inputs=hr_resolution_preview_inputs,

+                outputs=[hr_final_resolution],

+                show_progress=False

+            )

+

+            for input in hr_resolution_preview_inputs:

+                input.change(**hr_resolution_preview_args)

+

             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)

             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)

 

@@ -791,6 +816,7 @@ def create_ui():
                 fn=lambda x: gr_show(x),

                 inputs=[enable_hr],

                 outputs=[hr_options],

+                show_progress = False,

             )

 

             txt2img_paste_fields = [

@@ -1792,7 +1818,7 @@ def create_ui():
                 if init_field is not None:

                     init_field(saved_value)

 

-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:

+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:

             apply_field(x, 'visible')

 

         if type(x) == gr.Slider:

@@ -1813,11 +1839,8 @@ def create_ui():
         if type(x) == gr.Number:

             apply_field(x, 'value')

 

-        # Since there are many dropdowns that shouldn't be saved,

-        # we only mark dropdowns that should be saved.

-        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):

+        if type(x) == gr.Dropdown:

             apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))

-            apply_field(x, 'visible')

 

     visit(txt2img_interface, loadsave, "txt2img")

     visit(img2img_interface, loadsave, "img2img")

diff --git a/modules/ui_components.py b/modules/ui_components.py
index 91eb0e3d..cac001dc 100644
--- a/modules/ui_components.py
+++ b/modules/ui_components.py
@@ -23,3 +23,11 @@ class FormGroup(gr.Group, gr.components.FormComponent):
 

     def get_block_name(self):

         return "group"

+

+

+class FormHTML(gr.HTML, gr.components.FormComponent):

+    """Same as gr.HTML but fits inside gradio forms"""

+

+    def get_block_name(self):

+        return "html"

+

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index eec9586f..742e745e 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -162,15 +162,15 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)

 

 

-def install_extension_from_index(url, hide_tags):

+def install_extension_from_index(url, hide_tags, sort_column):

     ext_table, message = install_extension_from_url(None, url)

 

-    code, _ = refresh_available_extensions_from_data(hide_tags)

+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)

 

     return code, ext_table, message

 

 

-def refresh_available_extensions(url, hide_tags):

+def refresh_available_extensions(url, hide_tags, sort_column):

     global available_extensions

 

     import urllib.request

@@ -179,18 +179,28 @@ def refresh_available_extensions(url, hide_tags):
 

     available_extensions = json.loads(text)

 

-    code, tags = refresh_available_extensions_from_data(hide_tags)

+    code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)

 

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

 

 

-def refresh_available_extensions_for_tags(hide_tags):

-    code, _ = refresh_available_extensions_from_data(hide_tags)

+def refresh_available_extensions_for_tags(hide_tags, sort_column):

+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)

 

     return code, ''

 

 

-def refresh_available_extensions_from_data(hide_tags):

+sort_ordering = [

+    # (reverse, order_by_function)

+    (True, lambda x: x.get('added', 'z')),

+    (False, lambda x: x.get('added', 'z')),

+    (False, lambda x: x.get('name', 'z')),

+    (True, lambda x: x.get('name', 'z')),

+    (False, lambda x: 'z'),

+]

+

+

+def refresh_available_extensions_from_data(hide_tags, sort_column):

     extlist = available_extensions["extensions"]

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

 

@@ -210,8 +220,11 @@ def refresh_available_extensions_from_data(hide_tags):
         <tbody>

     """

 

-    for ext in extlist:

+    sort_reverse, sort_function = sort_ordering[sort_column if 0 <= sort_column < len(sort_ordering) else 0]

+

+    for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):

         name = ext.get("name", "noname")

+        added = ext.get('added', 'unknown')

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

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

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

@@ -233,7 +246,7 @@ def refresh_available_extensions_from_data(hide_tags):
         code += f"""

             <tr>

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

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

+                <td>{html.escape(description)}<p class="info"><span class="date_added">Added: {html.escape(added)}</span></p></td>

                 <td>{install_code}</td>

             </tr>

         

@@ -291,25 +304,32 @@ def create_ui():
 

                 with gr.Row():

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

+                    sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")

 

                 install_result = gr.HTML()

                 available_extensions_table = gr.HTML()

 

                 refresh_available_extensions_button.click(

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

-                    inputs=[available_extensions_index, hide_tags],

+                    inputs=[available_extensions_index, hide_tags, sort_column],

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

                 )

 

                 install_extension_button.click(

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

-                    inputs=[extension_to_install, hide_tags],

+                    inputs=[extension_to_install, hide_tags, sort_column],

                     outputs=[available_extensions_table, extensions_table, install_result],

                 )

 

                 hide_tags.change(

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

-                    inputs=[hide_tags],

+                    inputs=[hide_tags, sort_column],

+                    outputs=[available_extensions_table, install_result]

+                )

+

+                sort_column.change(

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

+                    inputs=[hide_tags, sort_column],

                     outputs=[available_extensions_table, install_result]

                 )

 

diff --git a/requirements.txt b/requirements.txt
index 4f09385f..e1dbf8e5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,4 +30,4 @@ inflection
 GitPython

 torchsde

 safetensors

-psutil; sys_platform == 'darwin'

+psutil

diff --git a/screenshot.png b/screenshot.png
index 86c3209f..47a1be4e 100644
--- a/screenshot.png
+++ b/screenshot.png
diff --git a/style.css b/style.css
index ee74d79e..76721756 100644
--- a/style.css
+++ b/style.css
@@ -555,7 +555,7 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
 

 /* Extensions */

 

-#tab_extensions table{

+#tab_extensions table``{

     border-collapse: collapse;

 }

 

@@ -581,6 +581,15 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     font-size: 95%;

 }

 

+#available_extensions .info{

+    margin: 0;

+}

+

+#available_extensions .date_added{

+    opacity: 0.85;

+    font-size: 90%;

+}

+

 #image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{

     min-width: auto;

     padding-left: 0.5em;

@@ -633,6 +642,23 @@ footer {
     opacity: 0.85;

 }

 

+#txtimg_hr_finalres{

+    min-height: 0 !important;

+    padding: .625rem .75rem;

+    margin-left: -0.75em

+

+}

+

+#txtimg_hr_finalres .resolution{

+    font-weight: bold;

+}

+

+#txt2img_checkboxes > div > div{

+    flex: 0;

+    white-space: nowrap;

+    min-width: auto;

+}

+

 /* The following handles localization for right-to-left (RTL) languages like Arabic.

 The rtl media type will only be activated by the logic in javascript/localization.js.

 If you change anything above, you need to make sure it is RTL compliant by just running

diff --git a/txt2img_Screenshot.png b/txt2img_Screenshot.png
deleted file mode 100644
index 6e2759a4..00000000
--- a/txt2img_Screenshot.png
+++ /dev/null
diff --git a/webui.py b/webui.py
index d89e0fb5..8737e593 100644
--- a/webui.py
+++ b/webui.py
@@ -4,7 +4,7 @@ import threading
 import time

 import importlib

 import signal

-import threading

+import re

 from fastapi import FastAPI

 from fastapi.middleware.cors import CORSMiddleware

 from fastapi.middleware.gzip import GZipMiddleware

@@ -13,6 +13,11 @@ from modules import import_hook, errors
 from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call

 from modules.paths import script_path

 

+import torch

+# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors

+if ".dev" in torch.__version__ or "+git" in torch.__version__:

+    torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)

+

 from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir

 import modules.codeformer_model as codeformer

 import modules.extras

@@ -182,12 +187,14 @@ def webui():
 

         sd_samplers.set_samplers()

 

+        modules.script_callbacks.script_unloaded_callback()

         extensions.list_extensions()

 

         localization.list_localizations(cmd_opts.localizations_dir)

 

         modelloader.forbid_loaded_nonbuiltin_upscalers()

         modules.scripts.reload_scripts()

+        modules.script_callbacks.model_loaded_callback(shared.sd_model)

         modelloader.load_upscalers()

 

         for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]: