add infotext entry for emphasis; put emphasis into a separate file, add an option to parse but still ignore emphasis

modules/infotext_utils.py

@@ -356,6 +356,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
         res["Cache FP16 weight for LoRA"] = False
 
+    if "Emphasis" not in res:
+        res["Emphasis"] = "Original"
+
     infotext_versions.backcompat(res)
 
     for key in skip_fields:

modules/processing.py

@@ -455,6 +455,7 @@ class StableDiffusionProcessing:
             self.height,
             opts.fp8_storage,
             opts.cache_fp16_weight,
+            opts.emphasis,
         )
 
     def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):

modules/sd_emphasis.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+import torch
+
+
+class Emphasis:
+    """Emphasis class decides how to death with (emphasized:1.1) text in prompts"""
+
+    name: str = "Base"
+    description: str = ""
+
+    tokens: list[list[int]]
+    """tokens from the chunk of the prompt"""
+
+    multipliers: torch.Tensor
+    """tensor with multipliers, once for each token"""
+
+    z: torch.Tensor
+    """output of cond transformers network (CLIP)"""
+
+    def after_transformers(self):
+        """Called after cond transformers network has processed the chunk of the prompt; this function should modify self.z to apply the emphasis"""
+
+        pass
+
+
+class EmphasisNone(Emphasis):
+    name = "None"
+    description = "disable the mechanism entirely and treat (:.1.1) as literal characters"
+
+
+class EmphasisIgnore(Emphasis):
+    name = "Ignore"
+    description = "treat all empasised words as if they have no emphasis"
+
+
+class EmphasisOriginal(Emphasis):
+    name = "Original"
+    description = "the orginal emphasis implementation"
+
+    def after_transformers(self):
+        original_mean = self.z.mean()
+        self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
+
+        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+        new_mean = self.z.mean()
+        self.z = self.z * (original_mean / new_mean)
+
+
+class EmphasisOriginalNoNorm(EmphasisOriginal):
+    name = "No norm"
+    description = "same as orginal, but without normalization (seems to work better for SDXL)"
+
+    def after_transformers(self):
+        self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
+
+
+def get_current_option(emphasis_option_name):
+    return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)
+
+
+def get_options_descriptions():
+    return ", ".join(f"{x.name}: {x.description}" for x in options)
+
+
+options = [
+    EmphasisNone,
+    EmphasisIgnore,
+    EmphasisOriginal,
+    EmphasisOriginalNoNorm,
+]

modules/sd_hijack_clip.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices, sd_hijack
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis
 from modules.shared import opts
 
 
@@ -88,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         Returns the list and the total number of tokens in the prompt.
         """
 
-        if opts.enable_emphasis:
+        if opts.emphasis != "None":
             parsed = prompt_parser.parse_prompt_attention(line)
         else:
             parsed = [[line, 1.0]]
@@ -249,6 +249,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
                 self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
+        if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
+            self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+
         if getattr(self.wrapped, 'return_pooled', False):
             return torch.hstack(zs), zs[0].pooled
         else:
@@ -274,14 +277,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
 
         pooled = getattr(z, 'pooled', None)
 
-        # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
-        batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
-        original_mean = z.mean()
-        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
-        new_mean = z.mean()
+        emphasis = sd_emphasis.get_current_option(opts.emphasis)()
+        emphasis.tokens = remade_batch_tokens
+        emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
+        emphasis.z = z
 
-        if not getattr(opts, "disable_normalize_embeddings", False):
-            z = z * (original_mean / new_mean)
+        emphasis.after_transformers()
+
+        z = emphasis.z
 
         if pooled is not None:
             z.pooled = pooled

modules/sd_hijack_clip_old.py

@@ -32,7 +32,7 @@ def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase,
 
                 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
+                mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
                 if mult_change is not None:
                     mult *= mult_change
                     i += 1

modules/shared_options.py

@@ -1,7 +1,7 @@
 import os
 import gradio as gr
 
-from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util
+from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util, sd_emphasis
 from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir, default_output_dir  # noqa: F401
 from modules.shared_cmd_options import cmd_opts
 from modules.options import options_section, OptionInfo, OptionHTML, categories
@@ -154,8 +154,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
     "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
-    "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-    "disable_normalize_embeddings": OptionInfo(False, "Disable normalize embeddings").info("Do not normalize embeddings after calculating emphasis. It can be expected to be effective in preventing artifacts in SDXL."),
+    "emphasis": OptionInfo("Original", "Emphasis mode", gr.Radio, lambda: {"choices": [x.name for x in sd_emphasis.options]}, infotext="Emphasis").info("makes it possible to make model to pay (more:1.1) or (less:0.9) attention to text when you use the syntax in prompt; " + sd_emphasis.get_options_descriptions()),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),