added token counter next to txt2img and img2img prompts

javascript/helpers.js

@@ -0,0 +1,13 @@
+// helper functions
+
+function debounce(func, wait_time) {
+	let timeout;
+	return function wrapped(...args) {
+		let call_function = () => {
+			clearTimeout(timeout);
+			func(...args)
+		}
+		clearTimeout(timeout);
+		timeout = setTimeout(call_function, wait_time);
+	};
+}

javascript/ui.js

@@ -183,4 +183,51 @@ onUiUpdate(function(){
     });
 
     json_elem.parentElement.style.display="none"
+
+	let debounce_time = 800
+	if (!txt2img_textarea) {
+		txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea")
+		txt2img_textarea?.addEventListener("input", debounce(submit_prompt_text.bind(null, "txt2img"), debounce_time))
+	}
+	if (!img2img_textarea) {
+		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea")
+		img2img_textarea?.addEventListener("input", debounce(submit_prompt_text.bind(null, "img2img"), debounce_time))
+    }
 })
+
+
+let txt2img_textarea, img2img_textarea = undefined;
+function submit_prompt_text(source, e) {
+	let prompt_text;
+	if (source == "txt2img")
+		prompt_text = txt2img_textarea.value;
+	else if (source == "img2img")
+		prompt_text = img2img_textarea.value;
+	if (!prompt_text)
+		return;
+	params = {
+		method: "POST",
+		headers: {
+			"Accept": "application/json",
+			"Content-type": "application/json"
+		},
+		body: JSON.stringify({data:[prompt_text]})
+	}
+	fetch('http://127.0.0.1:7860/api/tokenize/', params)
+	.then((response) => response.json())
+	.then((data) => {
+		if (data?.data.length) {
+			let response_json = data.data[0]
+			if (elem = gradioApp().getElementById(source+"_token_counter")) {
+				if (response_json.token_count > response_json.max_length)
+					elem.classList.add("red");
+				else
+					elem.classList.remove("red");
+				elem.innerText = response_json.token_count + "/" + response_json.max_length;
+			}
+		}
+	})
+	.catch((error) => {
+		console.error('Error:', error);
+	});
+}

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 +270,11 @@ 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 {"tokens": remade_batch_tokens[0], "token_count":token_count, "max_length":max_length}
+        
 
 class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
     def __init__(self, wrapped, hijack):
@@ -294,14 +301,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
             if mult != 1.0:
                 self.token_mults[ident] = mult
 
-    def forward(self, text):
+    def process_text(self, text):
-        self.hijack.fixes = []
-        self.hijack.comments = []
-        remade_batch_tokens = []
         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 +362,8 @@ 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))
-
+                    hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-                    self.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 +372,14 @@ 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]))

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
@@ -337,11 +338,15 @@ 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=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")
+                    token_output = gr.JSON(visible=False)
+                    if is_img2img:  # only define the api function ONCE
+                        token_counter.change(fn=model_hijack.tokenize, api_name="tokenize", inputs=[token_counter], outputs=[token_output])
 
                 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)

style.css

@@ -389,3 +389,7 @@ input[type="range"]{
   border-radius: 8px;
   display: none;
 }
+
+.red {
+	color: red;
+}