mirror of
https://github.com/imrayya/stable-diffusion-webui-Prompt_Generator.git
synced 2024-01-11 09:00:44 +01:00
Made the results UI dynamic Feature Request: "Send to X" buttons for prompts. #20
This commit is contained in:
+115
-71
@@ -10,18 +10,24 @@ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLI
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"""
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import json
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import re
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import gradio as gr
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import modules
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from modules import script_callbacks
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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import re
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import json
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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result_prompt = ""
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models = {}
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max_no_results = 20 # TODO move to setting panel
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class Model:
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'''
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Small strut to hold data for the text generator
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'''
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def __init__(self, name, model, tokenizer) -> None:
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self.name = name
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self.model = model
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@@ -30,6 +36,8 @@ class Model:
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def populate_models():
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"""Get the models that this extension can use via models.json
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"""
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path = "./extensions/stable-diffusion-webui-Prompt_Generator/models.json"
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with open(path, 'r') as f:
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data = json.load(f)
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@@ -40,15 +48,8 @@ def populate_models():
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models[name] = Model(name, model, tokenizer)
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def add_to_prompt(num): # A function that determines which prompt to pass
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hand_over_prompt_list = result_prompt.splitlines()
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try:
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return (hand_over_prompt_list[int(num)-1][3:])
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except Exception as e:
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print(
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f"That line does not exist. Check number of prompts: {e}")
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return gr.update(), f"Error: {e}"
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def add_to_prompt(prompt): # A holder TODO figure out how to get rid of it
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return prompt
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def get_list_blacklist():
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@@ -65,42 +66,73 @@ def get_list_blacklist():
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def on_ui_tabs():
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# Method to create the extended prompt
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def generate_longer_generic(prompt, temperature, top_k,
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max_length, repetition_penalty, num_return_sequences, name, use_punctuation=False, use_blacklist=False):
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max_length, repetition_penalty,
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num_return_sequences, name, use_punctuation=False,
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use_blacklist=False, progress=gr.Progress()): # TODO make the progress bar work
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"""Generates a longer string from the input
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Args:
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prompt (str): As the name suggests, the start of the prompt that the generator should start with.
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temperature (float): A higher temperature will produce more diverse results, but with a higher risk of less coherent text
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top_k (float): Strategy is to sample from a shortlist of the top K tokens. This approach allows the other high-scoring tokens a chance of being picked.
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max_length (int): the maximum number of tokens for the output of the model
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repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty. Default setting is 1.2
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num_return_sequences (int): The number of results to generate
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name (str): Which Model to use
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use_punctuation (bool): Allows the use of commas in the output. Defaults to False.
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use_blacklist (bool): It will delete any matches to the generated result (case insensitive). Each item to be filtered out should be on a new line. Defaults to False.
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Returns:
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Returns only an error otherwise saves it in result_prompt
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"""
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progress(0, "Starting")
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try:
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progress(0.25)
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print("Loading Tokenizer")
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tokenizer = GPT2Tokenizer.from_pretrained(models[name].tokenizer)
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tokenizer.add_special_tokens({'pad_token': '[PAD]'})
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# Full credits for the model to FredZhang7 (https://huggingface.co/FredZhang7). Under creativeml-openrail-m license.
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progress(0.5)
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print("Loading Model")
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model = GPT2LMHeadModel.from_pretrained(models[name].model)
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except Exception as e:
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print(f"Exception encountered while attempting to install tokenizer")
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return gr.update(), f"Error: {e}"
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try:
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print(f"Generate new prompt from: \"{prompt}\" with {name}")
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progress(0.75)
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input_ids = tokenizer(prompt, return_tensors='pt').input_ids
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if(use_punctuation):
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if (use_punctuation):
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output = model.generate(input_ids, do_sample=True, temperature=temperature,
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top_k=round(top_k), max_length=max_length,
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num_return_sequences=num_return_sequences,
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repetition_penalty=float(
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repetition_penalty),
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early_stopping=True)
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top_k=round(top_k), max_length=max_length,
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num_return_sequences=num_return_sequences,
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repetition_penalty=float(
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repetition_penalty),
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early_stopping=True)
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else:
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output = model.generate(input_ids, do_sample=True, temperature=temperature,
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top_k=round(top_k), max_length=max_length,
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num_return_sequences=num_return_sequences,
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repetition_penalty=float(
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repetition_penalty),
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penalty_alpha=0.6, no_repeat_ngram_size=1,
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early_stopping=True)
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top_k=round(top_k), max_length=max_length,
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num_return_sequences=num_return_sequences,
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repetition_penalty=float(
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repetition_penalty),
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penalty_alpha=0.6, no_repeat_ngram_size=1,
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early_stopping=True)
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print("Generation complete!")
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progress(1, "Done!")
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tempString = ""
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if (use_blacklist):
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blacklist = get_list_blacklist()
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for i in range(len(output)):
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tempString += str(i+1)+": "+tokenizer.decode(
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tempString += tokenizer.decode(
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output[i], skip_special_tokens=True) + "\n"
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if (use_blacklist):
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@@ -112,20 +144,30 @@ def on_ui_tabs():
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result_prompt = tempString
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# print(result_prompt)
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return {results: tempString,
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send_to_img2img: gr.update(visible=True),
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send_to_txt2img: gr.update(visible=True),
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send_to_text: gr.update(visible=True),
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results_col: gr.update(visible=True),
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warning: gr.update(visible=True),
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promptNum_col: gr.update(visible=True)
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}
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except Exception as e:
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print(
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f"Exception encountered while attempting to generate prompt: {e}")
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return gr.update(), f"Error: {e}"
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def ui_dynamic_result_visible(num):
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"""Makes the results visible"""
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k = int(num)
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return [gr.Row.update(visible=True)]*k + [gr.Row.update(visible=False)]*(max_no_results-k)
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def ui_dynamic_result_prompts():
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"""Populates the results with the prompts"""
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lines = result_prompt.splitlines()
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num = len(lines)
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result_list = []
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for i in range(int(max_no_results)):
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if (i < num):
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result_list.append(lines[i])
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else:
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result_list.append("")
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return result_list
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# ----------------------------------------------------------------------------
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# UI structure
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txt2img_prompt = modules.ui.txt2img_paste_fields[0][0]
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img2img_prompt = modules.ui.img2img_paste_fields[0][0]
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@@ -150,7 +192,7 @@ def on_ui_tabs():
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repetition_penalty_slider = gr.Slider(
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elem_id="repetition_penalty_slider", label="Repetition Penalty", value=1.2, minimum=0.1, maximum=10, interactive=True)
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num_return_sequences_slider = gr.Slider(
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elem_id="num_return_sequences_slider", label="How Many To Generate", value=5, minimum=1, maximum=20, interactive=True, step=1)
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elem_id="num_return_sequences_slider", label="How Many To Generate", value=5, minimum=1, maximum=max_no_results, interactive=True, step=1)
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with gr.Column():
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with gr.Row():
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use_blacklist_checkbox = gr.Checkbox(label="Use blacklist?")
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@@ -158,43 +200,45 @@ def on_ui_tabs():
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with gr.Column():
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with gr.Row():
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populate_models()
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generate_dropdown = gr.Dropdown(choices=list(models.keys()), value="FredZhang7", label = "Which model to use?",show_label=True)
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generate_dropdown = gr.Dropdown(choices=list(models.keys()), value=list(models.keys())[
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1 if len(models) > 0 else 0], label="Which model to use?", show_label=True) # TODO Add default to setting page
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use_punctuation_check = gr.Checkbox(label="Use punctuation?")
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generateButton_fred = gr.Button(
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value="Generate", elem_id="generate_button")
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with gr.Column(visible=False) as results_col:
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results = gr.Text(
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label="Results", elem_id="Results_textBox", interactive=False)
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with gr.Column(visible=False) as promptNum_col:
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with gr.Row():
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promptNum = gr.Textbox(
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lines=1, elem_id="promptNum", label="Send which prompt")
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with gr.Column():
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warning = gr.HTML(
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value="Select one number and send that prompt to txt2img or img2img", visible=False)
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with gr.Row():
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send_to_txt2img = gr.Button('Send to txt2img', visible=False)
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send_to_img2img = gr.Button('Send to img2img', visible=False)
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send_to_text = gr.Button(
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'Send to back to prompter', visible=False)
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generateButton = gr.Button(
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value="Generate", elem_id="generate_button") # TODO Add element to show that it is working in the background so users don't think nothing is happening
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# events
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generateButton_fred.click(fn=generate_longer_generic, inputs=[
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# Handles Dynamic results
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results_vis = []
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results_txt_list = []
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with gr.Column() as results_col:
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for i in range(max_no_results):
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with gr.Row(visible=False) as row:
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row.style(equal_height=True) # Doesn't seem to do anything
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with gr.Column(scale=3): # Guessing at the scale
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textBox = gr.Textbox(label="")
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with gr.Column(scale=1):
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txt2img = gr.Button("send to txt2img")
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img2img = gr.Button("send to img2img")
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# Handles ___2img buttons
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txt2img.click(add_to_prompt, inputs=[
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textBox], outputs=[txt2img_prompt]).then(None, _js='switch_to_txt2img',
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inputs=None, outputs=None)
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img2img.click(add_to_prompt, inputs=[
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textBox], outputs=[img2img_prompt]).then(None, _js='switch_to_img2img',
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inputs=None, outputs=None)
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results_txt_list.append(textBox)
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results_vis.append(row)
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# ----------------------------------------------------------------------------------
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# Handle buttons
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#Please note that we use `.then()` to run other ui elements after the generation is done
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generateButton.click(fn=generate_longer_generic, inputs=[
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promptTxt, temp_slider, top_k_slider, max_length_slider,
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repetition_penalty_slider, num_return_sequences_slider,
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generate_dropdown,use_punctuation_check, use_blacklist_checkbox],
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outputs=[results, send_to_img2img, send_to_txt2img, send_to_text,
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results_col, warning, promptNum_col])
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send_to_img2img.click(add_to_prompt, inputs=[
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promptNum], outputs=[img2img_prompt])
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send_to_txt2img.click(add_to_prompt, inputs=[
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promptNum], outputs=[txt2img_prompt])
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send_to_text.click(add_to_prompt, inputs=[
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promptNum], outputs=[promptTxt])
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send_to_txt2img.click(None, _js='switch_to_txt2img',
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inputs=None, outputs=None)
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send_to_img2img.click(None, _js="switch_to_img2img",
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inputs=None, outputs=None)
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generate_dropdown, use_punctuation_check, use_blacklist_checkbox]).then(
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fn=ui_dynamic_result_visible, inputs=num_return_sequences_slider,
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outputs=results_vis).then(
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fn=ui_dynamic_result_prompts, outputs=results_txt_list)
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return (prompt_generator, "Prompt Generator", "Prompt Generator"),
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