add textual inversion hashes to infotext

modules/processing.py

@@ -732,10 +732,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             p.setup_conds()
 
-            if len(model_hijack.comments) > 0:
             for comment in model_hijack.comments:
                 comments[comment] = 1
 
+            p.extra_generation_params.update(model_hijack.extra_generation_params)
+
             if p.n_iter > 1:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 

modules/sd_hijack.py

@@ -147,7 +147,6 @@ def undo_weighted_forward(sd_model):
 
 class StableDiffusionModelHijack:
     fixes = None
-    comments = []
     layers = None
     circular_enabled = False
     clip = None
@@ -156,6 +155,9 @@ class StableDiffusionModelHijack:
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
 
     def __init__(self):
+        self.extra_generation_params = {}
+        self.comments = []
+
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
     def apply_optimizations(self, option=None):
@@ -236,6 +238,7 @@ class StableDiffusionModelHijack:
 
     def clear_comments(self):
         self.comments = []
+        self.extra_generation_params = {}
 
     def get_prompt_lengths(self, text):
         if self.clip is None:

modules/sd_hijack_clip.py

@@ -229,9 +229,18 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
             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}")
+        if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
+            hashes = []
+            for name, embedding in used_embeddings.items():
+                shorthash = embedding.shorthash
+                if not shorthash:
+                    continue
+
+                name = name.replace(":", "").replace(",", "")
+                hashes.append(f"{name}: {shorthash}")
+
+            if hashes:
+                self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
 
         return torch.hstack(zs)
 

modules/shared.py

@@ -472,6 +472,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
     "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_restart(),
+    "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
     "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
 }))
 

modules/textual_inversion/textual_inversion.py

@@ -13,7 +13,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from torch.utils.tensorboard import SummaryWriter
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -49,6 +49,8 @@ class Embedding:
         self.sd_checkpoint_name = None
         self.optimizer_state_dict = None
         self.filename = None
+        self.hash = None
+        self.shorthash = None
 
     def save(self, filename):
         embedding_data = {
@@ -82,6 +84,10 @@ class Embedding:
         self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
         return self.cached_checksum
 
+    def set_hash(self, v):
+        self.hash = v
+        self.shorthash = self.hash[0:12]
+
 
 class DirWithTextualInversionEmbeddings:
     def __init__(self, path):
@@ -199,6 +205,7 @@ class EmbeddingDatabase:
         embedding.vectors = vec.shape[0]
         embedding.shape = vec.shape[-1]
         embedding.filename = path
+        embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
 
         if self.expected_shape == -1 or self.expected_shape == embedding.shape:
             self.register_embedding(embedding, shared.sd_model)

style.css

@@ -231,6 +231,10 @@ button.custom-button{
     padding-top: 0.5em;
 }
 
+.html-log .comments:empty{
+    padding-top: 0;
+}
+
 .html-log .performance {
     font-size: 0.85em;
     color: #444;