Merge pull request #5 from AUTOMATIC1111/master

sync 01-07-2023

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>
+

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?")

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:

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:

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):

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)
 
-                    remade_tokens = remade_tokens[:last_comma]
-                    length = len(remade_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
 
-                    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
+                    reloc_tokens = chunk.tokens[break_location:]
+                    reloc_mults = chunk.multipliers[break_location:]
 
+                    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
+                    chunk.tokens.append(token)
+                    chunk.multipliers.append(weight)
+                    position += 1
+                    continue
 
-        token_count = len(remade_tokens)
-        prompt_target_length = get_target_prompt_token_count(token_count)
-        tokens_to_add = prompt_target_length - len(remade_tokens)
+                emb_len = int(embedding.vec.shape[0])
+                if len(chunk.tokens) + emb_len > self.chunk_length:
+                    next_chunk()
 
-        remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
-        multipliers = multipliers + [1.0] * tokens_to_add
+                chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
 
-        return remade_tokens, fixes, multipliers, token_count
+                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.
+        """
 
-    def process_text(self, texts):
-        used_custom_terms = []
-        remade_batch_tokens = []
-        hijack_comments = []
-        hijack_fixes = []
         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
+        batch_chunks, token_count = self.process_texts(texts)
 
-                i = 0
-                while i < len(tokens):
-                    token = tokens[i]
+        used_embeddings = {}
+        chunk_count = max([len(x) for x in batch_chunks])
 
-                    embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+        zs = []
+        for i in range(chunk_count):
+            batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
 
-                    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
+            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]
 
-                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")
+            for fixes in self.hijack.fixes:
+                for position, embedding in fixes:
+                    used_embeddings[embedding.name] = embedding
 
-                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)
+            z = self.process_tokens(tokens, multipliers)
+            zs.append(z)
 
-            multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
-            multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+        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}")
 
-            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
-
-        return z
+        return torch.hstack(zs)
 
     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]
-
+        """
+        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()

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)

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

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")
@@ -430,7 +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)),
 }))
 

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

modules/textual_inversion/textual_inversion.py

@@ -79,7 +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]

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
 
@@ -638,7 +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}"):
@@ -709,6 +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:
@@ -732,6 +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)
 
@@ -793,6 +816,7 @@ def create_ui():
                 fn=lambda x: gr_show(x),
                 inputs=[enable_hr],
                 outputs=[hr_options],
+                show_progress = False,
             )
 
             txt2img_paste_fields = [
@@ -1794,7 +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:
@@ -1815,11 +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")

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"
+

style.css

@@ -642,6 +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

webui.py

@@ -16,7 +16,7 @@ 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.]+', torch.__version__).group(0)
+    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