text-generation-webui/extensions/sd_api_pictures/script.py

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import base64
import io
import re
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import time
from datetime import date
from pathlib import Path
import gradio as gr
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import requests
import torch
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from PIL import Image
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import modules.shared as shared
from modules.models import reload_model, unload_model
torch._C._jit_set_profiling_mode(False)
# parameters which can be customized in settings.json of webui
params = {
'address': 'http://127.0.0.1:7860',
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'mode': 0, # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on)
'manage_VRAM': False,
'save_img': False,
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'SD_model': 'NeverEndingDream', # not used right now
'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful',
'negative_prompt': '(worst quality, low quality:1.3)',
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'width': 512,
'height': 512,
'denoising_strength': 0.61,
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'restore_faces': False,
'enable_hr': False,
'hr_upscaler': 'ESRGAN_4x',
'hr_scale': '1.0',
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'seed': -1,
'sampler_name': 'DDIM',
'steps': 32,
'cfg_scale': 7
}
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def give_VRAM_priority(actor):
global shared, params
if actor == 'SD':
unload_model()
print("Requesting Auto1111 to re-load last checkpoint used...")
response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
response.raise_for_status()
elif actor == 'LLM':
print("Requesting Auto1111 to vacate VRAM...")
response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
response.raise_for_status()
reload_model()
elif actor == 'set':
print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...")
response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
response.raise_for_status()
elif actor == 'reset':
print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint")
response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
response.raise_for_status()
else:
raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!')
response.raise_for_status()
del response
if params['manage_VRAM']:
give_VRAM_priority('set')
samplers = ['DDIM', 'DPM++ 2M Karras'] # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers
SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
picture_response = False # specifies if the next model response should appear as a picture
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def remove_surrounded_chars(string):
# this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
# 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
return re.sub('\*[^\*]*?(\*|$)', '', string)
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def triggers_are_in(string):
string = remove_surrounded_chars(string)
# regex searches for send|main|message|me (at the end of the word) followed by
# a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s),
# (?aims) are regex parser flags
return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string))
def state_modifier(state):
if picture_response:
state['stream'] = False
return state
def input_modifier(string):
"""
This function is applied to your text inputs before
they are fed into the model.
"""
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global params
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if not params['mode'] == 1: # if not in immersive/interactive mode, do nothing
return string
if triggers_are_in(string): # if we're in it, check for trigger words
toggle_generation(True)
string = string.lower()
if "of" in string:
subject = string.split('of', 1)[1] # subdivide the string once by the first 'of' instance and get what's coming after it
string = "Please provide a detailed and vivid description of " + subject
else:
string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now"
return string
# Get and save the Stable Diffusion-generated picture
def get_SD_pictures(description):
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global params
if params['manage_VRAM']:
give_VRAM_priority('SD')
payload = {
"prompt": params['prompt_prefix'] + description,
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"seed": params['seed'],
"sampler_name": params['sampler_name'],
"enable_hr": params['enable_hr'],
"hr_scale": params['hr_scale'],
"hr_upscaler": params['hr_upscaler'],
"denoising_strength": params['denoising_strength'],
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"steps": params['steps'],
"cfg_scale": params['cfg_scale'],
"width": params['width'],
"height": params['height'],
"restore_faces": params['restore_faces'],
"override_settings_restore_afterwards": True,
"negative_prompt": params['negative_prompt']
}
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print(f'Prompting the image generator via the API on {params["address"]}...')
response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
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response.raise_for_status()
r = response.json()
visible_result = ""
for img_str in r['images']:
if params['save_img']:
img_data = base64.b64decode(img_str)
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variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}'
output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
output_file.parent.mkdir(parents=True, exist_ok=True)
with open(output_file.as_posix(), 'wb') as f:
f.write(img_data)
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visible_result = visible_result + f'<img src="/file/extensions/sd_api_pictures/outputs/{variadic}.png" alt="{description}" style="max-width: unset; max-height: unset;">\n'
else:
image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0])))
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# lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
image.thumbnail((300, 300))
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
buffered.seek(0)
image_bytes = buffered.getvalue()
img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
if params['manage_VRAM']:
give_VRAM_priority('LLM')
return visible_result
# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
# and replace it with 'text' for the purposes of logging?
def output_modifier(string):
"""
This function is applied to the model outputs.
"""
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global picture_response, params
if not picture_response:
return string
string = remove_surrounded_chars(string)
string = string.replace('"', '')
string = string.replace('', '')
string = string.replace('\n', ' ')
string = string.strip()
if string == '':
string = 'no viable description in reply, try regenerating'
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return string
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text = ""
if (params['mode'] < 2):
toggle_generation(False)
text = f'*Sends a picture which portrays: “{string}”*'
else:
text = string
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string = get_SD_pictures(string) + "\n" + text
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return string
def bot_prefix_modifier(string):
"""
This function is only applied in chat mode. It modifies
the prefix text for the Bot and can be used to bias its
behavior.
"""
return string
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def toggle_generation(*args):
global picture_response, shared
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if not args:
picture_response = not picture_response
else:
picture_response = args[0]
shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*"
def filter_address(address):
address = address.strip()
# address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash
address = re.sub('\/$', '', address) # remove trailing /s
if not address.startswith('http'):
address = 'http://' + address
return address
def SD_api_address_update(address):
global params
msg = "✔️ SD API is found on:"
address = filter_address(address)
params.update({"address": address})
try:
response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models')
response.raise_for_status()
# r = response.json()
except:
msg = "❌ No SD API endpoint on:"
return gr.Textbox.update(label=msg)
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def ui():
# Gradio elements
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# gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title
with gr.Accordion("Parameters", open=True, elem_classes="SDAP"):
with gr.Row():
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address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address')
modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"]
mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index")
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with gr.Column(scale=1, min_width=300):
manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM')
save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat')
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force_pic = gr.Button("Force the picture response")
suppr_pic = gr.Button("Suppress the picture response")
with gr.Accordion("Generation parameters", open=False):
prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
with gr.Row():
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with gr.Column():
width = gr.Slider(256, 768, value=params['width'], step=64, label='Width')
height = gr.Slider(256, 768, value=params['height'], step=64, label='Height')
with gr.Column():
sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampling method', elem_id="sampler_box")
steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps")
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with gr.Row():
seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box")
cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box")
with gr.Column() as hr_options:
restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces')
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enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix')
with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options:
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hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by')
denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength')
hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler')
# Event functions to update the parameters in the backend
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address.change(lambda x: params.update({"address": filter_address(x)}), address, None)
mode.select(lambda x: params.update({"mode": x}), mode, None)
mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None)
manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None)
manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None)
save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
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address.submit(fn=SD_api_address_update, inputs=address, outputs=address)
prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None)
negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None)
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width.change(lambda x: params.update({"width": x}), width, None)
height.change(lambda x: params.update({"height": x}), height, None)
hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None)
denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None)
restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None)
hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None)
enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None)
enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options)
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sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None)
steps.change(lambda x: params.update({"steps": x}), steps, None)
seed.change(lambda x: params.update({"seed": x}), seed, None)
cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None)
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force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None)
suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None)