Merge pull request #1194 from liamkerr/token_count

Token count

2 files changed, 30 insertions, 9 deletions

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 7b2030d4..bfbd07f9 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -180,6 +180,7 @@ class StableDiffusionModelHijack:
     dir_mtime = None

     layers = None

     circular_enabled = False

+    clip = None

 

     def load_textual_inversion_embeddings(self, dirname, model):

         mt = os.path.getmtime(dirname)

@@ -242,6 +243,7 @@ class StableDiffusionModelHijack:
 

         model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)

         m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)

+        self.clip = m.cond_stage_model

 

         if cmd_opts.opt_split_attention_v1:

             ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1

@@ -268,6 +270,10 @@ class StableDiffusionModelHijack:
         for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:

             layer.padding_mode = 'circular' if enable else 'zeros'

 

+    def tokenize(self, text):

+        max_length = self.clip.max_length - 2

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

+        return remade_batch_tokens[0], token_count, max_length

 

 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):

     def __init__(self, wrapped, hijack):

@@ -294,14 +300,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:

                 self.token_mults[ident] = mult

 

-    def forward(self, text):

-        self.hijack.fixes = []

-        self.hijack.comments = []

-        remade_batch_tokens = []

+    def process_text(self, text):

         id_start = self.wrapped.tokenizer.bos_token_id

         id_end = self.wrapped.tokenizer.eos_token_id

         maxlen = self.wrapped.max_length

         used_custom_terms = []

+        remade_batch_tokens = []

+        overflowing_words = []

+        hijack_comments = []

+        hijack_fixes = []

+        token_count = 0

 

         cache = {}

         batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]

@@ -353,9 +361,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     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))

-

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

-

+                    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)

@@ -364,8 +371,14 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]

 

             remade_batch_tokens.append(remade_tokens)

-            self.hijack.fixes.append(fixes)

+            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):

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

+        self.hijack.fixes = hijack_fixes

+        self.hijack.comments = hijack_comments

 

         if len(used_custom_terms) > 0:

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

diff --git a/modules/ui.py b/modules/ui.py
index ab28c84d..bf736b27 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -22,6 +22,7 @@ from modules.paths import script_path
 from modules.shared import opts, cmd_opts

 import modules.shared as shared

 from modules.sd_samplers import samplers, samplers_for_img2img

+from modules.sd_hijack import model_hijack

 import modules.ldsr_model

 import modules.scripts

 import modules.gfpgan_model

@@ -333,6 +334,10 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]

     )

 

+def update_token_counter(text):

+    tokens, token_count, max_length = model_hijack.tokenize(text)

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

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

 

 def create_toprow(is_img2img):

     id_part = "img2img" if is_img2img else "txt2img"

@@ -342,11 +347,14 @@ def create_toprow(is_img2img):
             with gr.Row():

                 with gr.Column(scale=80):

                     with gr.Row():

-                        prompt = gr.Textbox(label="Prompt", elem_id="prompt", show_label=False, placeholder="Prompt", lines=2)

+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)

 

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

                     roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)

                     paste = gr.Button(value=paste_symbol, elem_id="paste")

+                    token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")

+                    hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")

+                    hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])

 

                 with gr.Column(scale=10, 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())), visible=len(shared.prompt_styles.styles) > 1)