AUTOMATIC1111
GitHub
commit
278e7c7114
@ -182,4 +182,23 @@ onUiUpdate(function(){
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});
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json_elem.parentElement.style.display="none"
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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", () => update_token_counter("txt2img_token_button"));
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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", () => update_token_counter("img2img_token_button"));
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}
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})
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let txt2img_textarea, img2img_textarea = undefined;
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let wait_time = 800
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let token_timeout;
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function update_token_counter(button_id) {
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if (token_timeout)
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clearTimeout(token_timeout);
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token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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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 +270,10 @@ 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 remade_batch_tokens[0], token_count, 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 +300,16 @@ 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 +361,8 @@ 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 +371,14 @@ 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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@ -333,6 +334,10 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
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outputs=[seed, dummy_component]
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)
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def update_token_counter(text):
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tokens, token_count, max_length = model_hijack.tokenize(text)
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style_class = ' class="red"' if (token_count > max_length) else ""
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return f"<span {style_class}>{token_count}/{max_length}</span>"
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def create_toprow(is_img2img):
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id_part = "img2img" if is_img2img else "txt2img"
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@ -342,11 +347,14 @@ 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=f"{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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hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
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hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
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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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