Spaces:
Runtime error
Runtime error
| # modules/lora_details.py | |
| import gradio as gr | |
| from modules.constants import LORA_DETAILS, LORAS | |
| def upd_prompt_notes_by_index(lora_index): | |
| """ | |
| Updates the prompt_notes_label with the notes from LORAS based on index. | |
| Args: | |
| lora_index (int): The index of the selected LoRA model. | |
| Returns: | |
| gr.update: Updated Gradio label component with the notes. | |
| """ | |
| try: | |
| if LORAS[lora_index]: | |
| notes = LORAS[lora_index].get('notes', None) | |
| if notes is None: | |
| trigger_word = LORAS[lora_index].get('trigger_word', "") | |
| trigger_position = LORAS[lora_index].get('trigger_position', "") | |
| notes = f"{trigger_position} '{trigger_word}' in prompt" | |
| except IndexError: | |
| notes = "Enter Prompt description of your image, \nusing models without LoRa may take a 30 minutes." | |
| return gr.update(value=notes) | |
| def get_trigger_words_by_index(lora_index): | |
| """ | |
| Retrieves the trigger words from LORAS for the specified index. | |
| Args: | |
| lora_index (int): The index of the selected LoRA model. | |
| Returns: | |
| str: The trigger words associated with the model, or an empty string if not found. | |
| """ | |
| try: | |
| trigger_words = LORAS[lora_index].get('trigger_word', "") | |
| except IndexError: | |
| trigger_words = "" | |
| return trigger_words | |
| def upd_prompt_notes(model_textbox_value): | |
| """ | |
| Updates the prompt_notes_label with the notes from LORA_DETAILS. | |
| Args: | |
| model_textbox_value (str): The name of the LoRA model. | |
| Returns: | |
| gr.update: Updated Gradio label component with the notes. | |
| """ | |
| notes = "" | |
| if model_textbox_value in LORA_DETAILS: | |
| lora_detail_list = LORA_DETAILS[model_textbox_value] | |
| for item in lora_detail_list: | |
| if 'notes' in item: | |
| notes = item['notes'] | |
| break | |
| else: | |
| notes = "Enter Prompt description of your image, \nusing models without LoRa may take a 30 minutes." | |
| return gr.update(value=notes) | |
| def get_trigger_words(model_textbox_value): | |
| """ | |
| Retrieves the trigger words from constants.LORA_DETAILS for the specified model. | |
| Args: | |
| model_textbox_value (str): The name of the LoRA model. | |
| Returns: | |
| str: The trigger words associated with the model, or a default message if not found. | |
| """ | |
| trigger_words = "" | |
| if model_textbox_value in LORA_DETAILS: | |
| lora_detail_list = LORA_DETAILS[model_textbox_value] | |
| for item in lora_detail_list: | |
| if 'trigger_words' in item: | |
| trigger_words = item['trigger_words'] | |
| break | |
| else: | |
| trigger_words = "" | |
| return trigger_words | |
| def upd_trigger_words(model_textbox_value): | |
| """ | |
| Updates the trigger_words_label with the trigger words from LORA_DETAILS. | |
| Args: | |
| model_textbox_value (str): The name of the LoRA model. | |
| Returns: | |
| gr.update: Updated Gradio label component with the trigger words. | |
| """ | |
| trigger_words = get_trigger_words(model_textbox_value) | |
| return gr.update(value=trigger_words) | |
| def approximate_token_count(prompt): | |
| """ | |
| Approximates the number of tokens in a prompt based on word count. | |
| Parameters: | |
| prompt (str): The text prompt. | |
| Returns: | |
| int: The approximate number of tokens. | |
| """ | |
| words = prompt.split() | |
| # Average tokens per word (can vary based on language and model) | |
| tokens_per_word = 1.3 | |
| return int(len(words) * tokens_per_word) | |
| def split_prompt_by_tokens(prompt, token_number): | |
| words = prompt.split() | |
| # Average tokens per word (can vary based on language and model) | |
| tokens_per_word = 1.3 | |
| return ' '.join(words[:int(tokens_per_word * token_number)]), ' '.join(words[int(tokens_per_word * token_number):]) | |
| # Split prompt precisely by token count | |
| import tiktoken | |
| def split_prompt_precisely(prompt, max_tokens=77, model="gpt-3.5-turbo"): | |
| try: | |
| encoding = tiktoken.encoding_for_model(model) | |
| except KeyError: | |
| encoding = tiktoken.get_encoding("cl100k_base") | |
| tokens = encoding.encode(prompt) | |
| if len(tokens) <= max_tokens: | |
| return prompt, "" | |
| # Find the split point | |
| split_point = max_tokens | |
| split_tokens = tokens[:split_point] | |
| remaining_tokens = tokens[split_point:] | |
| split_prompt = encoding.decode(split_tokens) | |
| remaining_prompt = encoding.decode(remaining_tokens) | |
| return split_prompt, remaining_prompt | |