import base64 import io import re import time from datetime import date from pathlib import Path import gradio as gr import requests import torch from PIL import Image from modules import shared from modules.models import reload_model, unload_model from modules.ui import create_refresh_button 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', '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, 'SD_model': 'NeverEndingDream', # not used right now 'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful', 'negative_prompt': '(worst quality, low quality:1.3)', 'width': 512, 'height': 512, 'denoising_strength': 0.61, 'restore_faces': False, 'enable_hr': False, 'hr_upscaler': 'ESRGAN_4x', 'hr_scale': '1.0', 'seed': -1, 'sampler_name': 'DPM++ 2M Karras', 'steps': 32, 'cfg_scale': 7, 'textgen_prefix': 'Please provide a detailed and vivid description of [subject]', 'sd_checkpoint': ' ', 'checkpoint_list': [" "] } 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') 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 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) 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. """ global params 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 = params['textgen_prefix'].replace("[subject]", subject) else: string = params['textgen_prefix'].replace("[subject]", "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, character): global params if params['manage_VRAM']: give_VRAM_priority('SD') description = re.sub('', ' ', description) description = f"({description}:1)" payload = { "prompt": params['prompt_prefix'] + description, "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'], "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'] } print(f'Prompting the image generator via the API on {params["address"]}...') response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload) 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) variadic = f'{date.today().strftime("%Y_%m_%d")}/{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) visible_result = visible_result + f'{description}\n' else: image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0]))) # 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'{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, state): """ This function is applied to the model outputs. """ 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' return string text = "" if (params['mode'] < 2): toggle_generation(False) text = f'*Sends a picture which portrays: “{string}”*' else: text = string string = get_SD_pictures(string, state['character_menu']) + "\n" + text 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 def toggle_generation(*args): global picture_response, shared 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) def custom_css(): path_to_css = Path(__file__).parent.resolve() / 'style.css' return open(path_to_css, 'r').read() def get_checkpoints(): global params try: models = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models') options = requests.get(url=f'{params["address"]}/sdapi/v1/options') options_json = options.json() params['sd_checkpoint'] = options_json['sd_model_checkpoint'] params['checkpoint_list'] = [result["title"] for result in models.json()] except: params['sd_checkpoint'] = "" params['checkpoint_list'] = [] return gr.update(choices=params['checkpoint_list'], value=params['sd_checkpoint']) def load_checkpoint(checkpoint): payload = { "sd_model_checkpoint": checkpoint } try: requests.post(url=f'{params["address"]}/sdapi/v1/options', json=payload) except: pass def get_samplers(): try: response = requests.get(url=f'{params["address"]}/sdapi/v1/samplers') response.raise_for_status() samplers = [x["name"] for x in response.json()] except: samplers = [] return samplers def ui(): # Gradio elements # 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(): 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") 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') force_pic = gr.Button("Force the picture response") suppr_pic = gr.Button("Suppress the picture response") with gr.Row(): checkpoint = gr.Dropdown(params['checkpoint_list'], value=params['sd_checkpoint'], label="Checkpoint", type="value") update_checkpoints = gr.Button("Get list of checkpoints") 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)') textgen_prefix = gr.Textbox(placeholder=params['textgen_prefix'], value=params['textgen_prefix'], label='textgen prefix (type [subject] where the subject should be placed)') negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt') with gr.Row(): with gr.Column(): width = gr.Slider(64, 2048, value=params['width'], step=64, label='Width') height = gr.Slider(64, 2048, value=params['height'], step=64, label='Height') with gr.Column(variant="compact", elem_id="sampler_col"): with gr.Row(elem_id="sampler_row"): sampler_name = gr.Dropdown(value=params['sampler_name'], label='Sampling method', elem_id="sampler_box") create_refresh_button(sampler_name, lambda: None, lambda: {'choices': get_samplers()}, 'refresh-button') steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps", elem_id="steps_box") 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') 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: 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 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) address.submit(fn=SD_api_address_update, inputs=address, outputs=address) prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None) textgen_prefix.change(lambda x: params.update({"textgen_prefix": x}), textgen_prefix, None) negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None) 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) update_checkpoints.click(get_checkpoints, None, checkpoint) checkpoint.change(lambda x: params.update({"sd_checkpoint": x}), checkpoint, None) checkpoint.change(load_checkpoint, checkpoint, None) 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) 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)