added token counter next to txt2img and img2img prompts
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Liam
parent
ca3e5519e8
commit
5034f7d759
13
javascript/helpers.js
Normal file
13
javascript/helpers.js
Normal file
@ -0,0 +1,13 @@
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// helper functions
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function debounce(func, wait_time) {
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let timeout;
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return function wrapped(...args) {
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let call_function = () => {
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clearTimeout(timeout);
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func(...args)
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}
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clearTimeout(timeout);
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timeout = setTimeout(call_function, wait_time);
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};
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}
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@ -183,4 +183,51 @@ onUiUpdate(function(){
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});
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json_elem.parentElement.style.display="none"
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let debounce_time = 800
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if (!txt2img_textarea) {
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txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea")
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txt2img_textarea?.addEventListener("input", debounce(submit_prompt_text.bind(null, "txt2img"), debounce_time))
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}
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if (!img2img_textarea) {
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img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea")
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img2img_textarea?.addEventListener("input", debounce(submit_prompt_text.bind(null, "img2img"), debounce_time))
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}
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})
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let txt2img_textarea, img2img_textarea = undefined;
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function submit_prompt_text(source, e) {
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let prompt_text;
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if (source == "txt2img")
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prompt_text = txt2img_textarea.value;
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else if (source == "img2img")
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prompt_text = img2img_textarea.value;
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if (!prompt_text)
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return;
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params = {
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method: "POST",
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headers: {
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"Accept": "application/json",
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"Content-type": "application/json"
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},
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body: JSON.stringify({data:[prompt_text]})
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}
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fetch('http://127.0.0.1:7860/api/tokenize/', params)
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.then((response) => response.json())
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.then((data) => {
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if (data?.data.length) {
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let response_json = data.data[0]
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if (elem = gradioApp().getElementById(source+"_token_counter")) {
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if (response_json.token_count > response_json.max_length)
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elem.classList.add("red");
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else
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elem.classList.remove("red");
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elem.innerText = response_json.token_count + "/" + response_json.max_length;
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}
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}
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})
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.catch((error) => {
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console.error('Error:', error);
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});
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}
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@ -180,6 +180,7 @@ class StableDiffusionModelHijack:
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dir_mtime = None
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layers = None
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circular_enabled = False
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clip = None
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def load_textual_inversion_embeddings(self, dirname, model):
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mt = os.path.getmtime(dirname)
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@ -242,6 +243,7 @@ class StableDiffusionModelHijack:
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model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
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m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
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self.clip = m.cond_stage_model
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if cmd_opts.opt_split_attention_v1:
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ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
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@ -268,6 +270,11 @@ class StableDiffusionModelHijack:
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for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
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layer.padding_mode = 'circular' if enable else 'zeros'
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def tokenize(self, text):
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max_length = self.clip.max_length - 2
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_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
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return {"tokens": remade_batch_tokens[0], "token_count":token_count, "max_length":max_length}
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class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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def __init__(self, wrapped, hijack):
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@ -294,14 +301,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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if mult != 1.0:
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self.token_mults[ident] = mult
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def forward(self, text):
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self.hijack.fixes = []
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self.hijack.comments = []
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remade_batch_tokens = []
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def process_text(self, text):
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id_start = self.wrapped.tokenizer.bos_token_id
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id_end = self.wrapped.tokenizer.eos_token_id
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maxlen = self.wrapped.max_length
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used_custom_terms = []
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remade_batch_tokens = []
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overflowing_words = []
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hijack_comments = []
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hijack_fixes = []
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token_count = 0
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cache = {}
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batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
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@ -353,9 +362,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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ovf = remade_tokens[maxlen - 2:]
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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token_count = len(remade_tokens)
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remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
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remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
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cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
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@ -364,8 +372,14 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
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multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
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remade_batch_tokens.append(remade_tokens)
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self.hijack.fixes.append(fixes)
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def forward(self, text):
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
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self.hijack.fixes = hijack_fixes
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self.hijack.comments = hijack_comments
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if len(used_custom_terms) > 0:
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self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
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@ -22,6 +22,7 @@ from modules.paths import script_path
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from modules.shared import opts, cmd_opts
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import modules.shared as shared
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.sd_hijack import model_hijack
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import modules.ldsr_model
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import modules.scripts
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import modules.gfpgan_model
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@ -337,11 +338,15 @@ def create_toprow(is_img2img):
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with gr.Row():
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with gr.Column(scale=80):
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", elem_id="prompt", show_label=False, placeholder="Prompt", lines=2)
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prompt = gr.Textbox(label="Prompt", elem_id=id_part+"_prompt", show_label=False, placeholder="Prompt", lines=2)
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with gr.Column(scale=1, elem_id="roll_col"):
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roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
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paste = gr.Button(value=paste_symbol, elem_id="paste")
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token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
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token_output = gr.JSON(visible=False)
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if is_img2img: # only define the api function ONCE
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token_counter.change(fn=model_hijack.tokenize, api_name="tokenize", inputs=[token_counter], outputs=[token_output])
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with gr.Column(scale=10, elem_id="style_pos_col"):
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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)
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