mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
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697 lines
37 KiB
Python
697 lines
37 KiB
Python
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
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# Copyright 2022 Sygil-Dev team.
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# base webui import and utils.
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from sd_utils import st, MemUsageMonitor, server_state, \
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get_next_sequence_number, check_prompt_length, torch_gc, \
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save_sample, generation_callback, process_images, \
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KDiffusionSampler, \
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custom_models_available, RealESRGAN_available, GFPGAN_available, \
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LDSR_available, load_models, hc, seed_to_int, logger
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# streamlit imports
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from streamlit.runtime.scriptrunner import StopException
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#streamlit components section
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import streamlit_nested_layout #used to allow nested columns, just importing it is enought
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#from streamlit.elements import image as STImage
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import streamlit.components.v1 as components
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#from streamlit.runtime.media_file_manager import media_file_manager
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from streamlit.elements.image import image_to_url
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#other imports
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import base64, uuid
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import os, sys, datetime, time
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from PIL import Image
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import requests
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from slugify import slugify
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from ldm.models.diffusion.ddim import DDIMSampler
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from typing import Union
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from io import BytesIO
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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# streamlit components
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from custom_components import sygil_suggestions
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# Temp imports
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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sygil_suggestions.init()
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except:
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pass
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#
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# Dev mode (server)
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# _component_func = components.declare_component(
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# "sd-gallery",
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# url="http://localhost:3001",
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# )
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# Init Vuejs component
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_component_func = components.declare_component(
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"sd-gallery", "./frontend/dists/sd-gallery/dist")
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def sdGallery(images=[], key=None):
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component_value = _component_func(images=imgsToGallery(images), key=key, default="")
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return component_value
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def imgsToGallery(images):
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urls = []
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for i in images:
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# random string for id
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random_id = str(uuid.uuid4())
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url = image_to_url(
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image=i,
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image_id= random_id,
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width=i.width,
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clamp=False,
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channels="RGB",
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output_format="PNG"
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)
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# image_io = BytesIO()
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# i.save(image_io, 'PNG')
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# width, height = i.size
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# image_id = "%s" % (str(images.index(i)))
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# (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
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# this_file = media_file_manager.add(data, mimetype, image_id)
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# img_str = this_file.url
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urls.append(url)
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return urls
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class plugin_info():
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plugname = "txt2img"
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description = "Text to Image"
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isTab = True
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displayPriority = 1
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@logger.catch(reraise=True)
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def stable_horde(outpath, prompt, seed, sampler_name, save_grid, batch_size,
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n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, GFPGAN_model,
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use_RealESRGAN, realesrgan_model_name, use_LDSR,
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LDSR_model_name, ddim_eta, normalize_prompt_weights,
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save_individual_images, sort_samples, write_info_files,
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jpg_sample, variant_amount, variant_seed, api_key,
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nsfw=True, censor_nsfw=False):
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log = []
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log.append("Generating image with Stable Horde.")
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st.session_state["progress_bar_text"].code('\n'.join(log), language='')
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# start time after garbage collection (or before?)
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start_time = time.time()
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# We will use this date here later for the folder name, need to start_time if not need
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run_start_dt = datetime.datetime.now()
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mem_mon = MemUsageMonitor('MemMon')
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mem_mon.start()
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os.makedirs(outpath, exist_ok=True)
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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params = {
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"sampler_name": "k_euler",
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"toggles": [1,4],
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"cfg_scale": cfg_scale,
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"seed": str(seed),
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"width": width,
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"height": height,
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"seed_variation": variant_seed if variant_seed else 1,
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"steps": int(steps),
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"n": int(n_iter)
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# You can put extra params here if you wish
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}
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final_submit_dict = {
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"prompt": prompt,
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"params": params,
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"nsfw": nsfw,
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"censor_nsfw": censor_nsfw,
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"trusted_workers": True,
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"workers": []
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}
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log.append(final_submit_dict)
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headers = {"apikey": api_key}
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logger.debug(final_submit_dict)
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st.session_state["progress_bar_text"].code('\n'.join(str(log)), language='')
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horde_url = "https://stablehorde.net"
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submit_req = requests.post(f'{horde_url}/api/v2/generate/async', json = final_submit_dict, headers = headers)
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if submit_req.ok:
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submit_results = submit_req.json()
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logger.debug(submit_results)
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log.append(submit_results)
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st.session_state["progress_bar_text"].code(''.join(str(log)), language='')
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req_id = submit_results['id']
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is_done = False
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while not is_done:
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chk_req = requests.get(f'{horde_url}/api/v2/generate/check/{req_id}')
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if not chk_req.ok:
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logger.error(chk_req.text)
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return
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chk_results = chk_req.json()
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logger.info(chk_results)
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is_done = chk_results['done']
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time.sleep(1)
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retrieve_req = requests.get(f'{horde_url}/api/v2/generate/status/{req_id}')
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if not retrieve_req.ok:
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logger.error(retrieve_req.text)
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return
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results_json = retrieve_req.json()
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# logger.debug(results_json)
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results = results_json['generations']
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output_images = []
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comments = []
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prompt_matrix_parts = []
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if not st.session_state['defaults'].general.no_verify_input:
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try:
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check_prompt_length(prompt, comments)
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except:
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import traceback
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logger.info("Error verifying input:", file=sys.stderr)
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logger.info(traceback.format_exc(), file=sys.stderr)
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all_prompts = batch_size * n_iter * [prompt]
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all_seeds = [seed + x for x in range(len(all_prompts))]
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for iter in range(len(results)):
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b64img = results[iter]["img"]
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base64_bytes = b64img.encode('utf-8')
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img_bytes = base64.b64decode(base64_bytes)
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img = Image.open(BytesIO(img_bytes))
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sanitized_prompt = slugify(prompt)
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prompts = all_prompts[iter * batch_size:(iter + 1) * batch_size]
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#captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
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seeds = all_seeds[iter * batch_size:(iter + 1) * batch_size]
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if sort_samples:
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full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)
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sanitized_prompt = sanitized_prompt[:200-len(full_path)]
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sample_path_i = os.path.join(sample_path, sanitized_prompt)
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#print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
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#print(os.path.join(os.getcwd(), sample_path_i))
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os.makedirs(sample_path_i, exist_ok=True)
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base_count = get_next_sequence_number(sample_path_i)
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filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[iter]}"
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else:
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full_path = os.path.join(os.getcwd(), sample_path)
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sample_path_i = sample_path
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base_count = get_next_sequence_number(sample_path_i)
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filename = f"{base_count:05}-{steps}_{sampler_name}_{seed}_{sanitized_prompt}"[:200-len(full_path)] #same as before
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save_sample(img, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img=None,
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denoising_strength=0.75, resize_mode=None, uses_loopback=False, uses_random_seed_loopback=False,
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save_grid=save_grid,
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sort_samples=sampler_name, sampler_name=sampler_name, ddim_eta=ddim_eta, n_iter=n_iter,
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batch_size=batch_size, i=iter, save_individual_images=save_individual_images,
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model_name="Stable Diffusion v1.5")
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output_images.append(img)
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# update image on the UI so we can see the progress
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if "preview_image" in st.session_state:
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st.session_state["preview_image"].image(img)
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if "progress_bar_text" in st.session_state:
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st.session_state["progress_bar_text"].empty()
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#if len(results) > 1:
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#final_filename = f"{iter}_{filename}"
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#img.save(final_filename)
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#logger.info(f"Saved {final_filename}")
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else:
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if "progress_bar_text" in st.session_state:
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st.session_state["progress_bar_text"].error(submit_req.text)
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logger.error(submit_req.text)
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mem_max_used, mem_total = mem_mon.read_and_stop()
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time_diff = time.time()-start_time
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info = f"""
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{prompt}
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Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN else ''}{', '+realesrgan_model_name if use_RealESRGAN else ''}
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{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
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stats = f'''
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Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
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Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
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for comment in comments:
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info += "\n\n" + comment
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#mem_mon.stop()
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#del mem_mon
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torch_gc()
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return output_images, seed, info, stats
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#
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@logger.catch(reraise=True)
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def txt2img(prompt: str, ddim_steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
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height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
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save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
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save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3', use_RealESRGAN: bool = False,
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RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", use_LDSR: bool = True, LDSR_model: str = "model",
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fp = None, variant_amount: float = 0.0,
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variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True,
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use_stable_horde: bool = False, stable_horde_key:str = "0000000000"):
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outpath = st.session_state['defaults'].general.outdir_txt2img
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seed = seed_to_int(seed)
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if not use_stable_horde:
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(server_state["model"])
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(server_state["model"])
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elif sampler_name == 'k_dpm_2_a':
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sampler = KDiffusionSampler(server_state["model"],'dpm_2_ancestral')
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elif sampler_name == 'k_dpm_2':
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sampler = KDiffusionSampler(server_state["model"],'dpm_2')
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elif sampler_name == 'k_euler_a':
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sampler = KDiffusionSampler(server_state["model"],'euler_ancestral')
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elif sampler_name == 'k_euler':
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sampler = KDiffusionSampler(server_state["model"],'euler')
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elif sampler_name == 'k_heun':
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sampler = KDiffusionSampler(server_state["model"],'heun')
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elif sampler_name == 'k_lms':
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sampler = KDiffusionSampler(server_state["model"],'lms')
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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def init():
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pass
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def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x,
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img_callback=generation_callback if not server_state["bridge"] else None,
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log_every_t=int(st.session_state.update_preview_frequency if not server_state["bridge"] else 100))
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return samples_ddim
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if use_stable_horde:
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output_images, seed, info, stats = stable_horde(
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prompt=prompt,
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seed=seed,
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outpath=outpath,
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sampler_name=sampler_name,
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save_grid=save_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=separate_prompts,
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use_GFPGAN=use_GFPGAN,
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GFPGAN_model=GFPGAN_model,
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use_RealESRGAN=use_RealESRGAN,
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realesrgan_model_name=RealESRGAN_model,
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use_LDSR=use_LDSR,
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LDSR_model_name=LDSR_model,
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ddim_eta=ddim_eta,
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normalize_prompt_weights=normalize_prompt_weights,
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save_individual_images=save_individual_images,
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sort_samples=group_by_prompt,
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write_info_files=write_info_files,
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jpg_sample=save_as_jpg,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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api_key=stable_horde_key
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)
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else:
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#try:
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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save_grid=save_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=separate_prompts,
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use_GFPGAN=use_GFPGAN,
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GFPGAN_model=GFPGAN_model,
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use_RealESRGAN=use_RealESRGAN,
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realesrgan_model_name=RealESRGAN_model,
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use_LDSR=use_LDSR,
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LDSR_model_name=LDSR_model,
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ddim_eta=ddim_eta,
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normalize_prompt_weights=normalize_prompt_weights,
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save_individual_images=save_individual_images,
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sort_samples=group_by_prompt,
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write_info_files=write_info_files,
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jpg_sample=save_as_jpg,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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)
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del sampler
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return output_images, seed, info, stats
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#except RuntimeError as e:
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#err = e
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#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
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#stats = err_msg
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#return [], seed, 'err', stats
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#
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@logger.catch(reraise=True)
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def layout():
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with st.form("txt2img-inputs"):
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st.session_state["generation_mode"] = "txt2img"
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input_col1, generate_col1 = st.columns([10,1])
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with input_col1:
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#prompt = st.text_area("Input Text","")
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placeholder = "A corgi wearing a top hat as an oil painting."
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prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
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sygil_suggestions.suggestion_area(placeholder)
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if "defaults" in st.session_state:
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if st.session_state['defaults'].admin.global_negative_prompt:
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prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"
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print(prompt)
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# creating the page layout using columns
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col1, col2, col3 = st.columns([2,5,2], gap="large")
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with col1:
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width = st.slider("Width:", min_value=st.session_state['defaults'].txt2img.width.min_value, max_value=st.session_state['defaults'].txt2img.width.max_value,
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value=st.session_state['defaults'].txt2img.width.value, step=st.session_state['defaults'].txt2img.width.step)
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height = st.slider("Height:", min_value=st.session_state['defaults'].txt2img.height.min_value, max_value=st.session_state['defaults'].txt2img.height.max_value,
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value=st.session_state['defaults'].txt2img.height.value, step=st.session_state['defaults'].txt2img.height.step)
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cfg_scale = st.number_input("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
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value=st.session_state['defaults'].txt2img.cfg_scale.value, step=st.session_state['defaults'].txt2img.cfg_scale.step,
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help="How strongly the image should follow the prompt.")
|
|
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|
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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|
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|
with st.expander("Batch Options"):
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|
#batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
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|
#value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
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|
#help="How many iterations or batches of images to generate in total.")
|
|
|
|
#batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
|
|
#value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
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|
#help="How many images are at once in a batch.\
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|
#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
|
|
#Default: 1")
|
|
|
|
st.session_state["batch_count"] = st.number_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
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|
help="How many iterations or batches of images to generate in total.")
|
|
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|
st.session_state["batch_size"] = st.number_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
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|
help="How many images are at once in a batch.\
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|
It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
|
|
to finish generation as more images are generated at once.\
|
|
Default: 1")
|
|
|
|
with st.expander("Preview Settings"):
|
|
|
|
st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview
|
|
st.session_state["update_preview_frequency"] = st.number_input("Update Image Preview Frequency",
|
|
min_value=0,
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|
value=st.session_state['defaults'].txt2img.update_preview_frequency,
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|
help="Frequency in steps at which the the preview image is updated. By default the frequency \
|
|
is set to 10 step.")
|
|
|
|
with col2:
|
|
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
|
|
|
|
with preview_tab:
|
|
#st.write("Image")
|
|
#Image for testing
|
|
#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
|
|
#new_image = image.resize((175, 240))
|
|
#preview_image = st.image(image)
|
|
|
|
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
|
|
st.session_state["preview_image"] = st.empty()
|
|
|
|
|
|
st.session_state["progress_bar_text"] = st.empty()
|
|
st.session_state["progress_bar_text"].info("Nothing but crickets here, try generating something first.")
|
|
|
|
st.session_state["progress_bar"] = st.empty()
|
|
|
|
message = st.empty()
|
|
|
|
with gallery_tab:
|
|
st.session_state["gallery"] = st.empty()
|
|
#st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")
|
|
|
|
with col3:
|
|
# If we have custom models available on the "models/custom"
|
|
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
|
|
custom_models_available()
|
|
|
|
if server_state["CustomModel_available"]:
|
|
st.session_state["custom_model"] = st.selectbox("Custom Model:", server_state["custom_models"],
|
|
index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model),
|
|
help="Select the model you want to use. This option is only available if you have custom models \
|
|
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
|
|
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
|
|
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5")
|
|
|
|
st.session_state.sampling_steps = st.number_input("Sampling Steps", value=st.session_state.defaults.txt2img.sampling_steps.value,
|
|
min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
|
|
step=st.session_state['defaults'].txt2img.sampling_steps.step,
|
|
help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)")
|
|
|
|
sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
|
|
sampler_name = st.selectbox("Sampling method", sampler_name_list,
|
|
index=sampler_name_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
|
|
|
|
with st.expander("Advanced"):
|
|
with st.expander("Stable Horde"):
|
|
use_stable_horde = st.checkbox("Use Stable Horde", value=False, help="Use the Stable Horde to generate images. More info can be found at https://stablehorde.net/")
|
|
stable_horde_key = st.text_input("Stable Horde Api Key", value=st.session_state['defaults'].general.stable_horde_api, type="password",
|
|
help="Optional Api Key used for the Stable Horde Bridge, if no api key is added the horde will be used anonymously.")
|
|
|
|
with st.expander("Output Settings"):
|
|
separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2img.separate_prompts,
|
|
help="Separate multiple prompts using the `|` character, and get all combinations of them.")
|
|
|
|
normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].txt2img.normalize_prompt_weights,
|
|
help="Ensure the sum of all weights add up to 1.0")
|
|
|
|
save_individual_images = st.checkbox("Save individual images.", value=st.session_state['defaults'].txt2img.save_individual_images,
|
|
help="Save each image generated before any filter or enhancement is applied.")
|
|
|
|
save_grid = st.checkbox("Save grid",value=st.session_state['defaults'].txt2img.save_grid, help="Save a grid with all the images generated into a single image.")
|
|
group_by_prompt = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2img.group_by_prompt,
|
|
help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
|
|
|
|
write_info_files = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2img.write_info_files,
|
|
help="Save a file next to the image with informartion about the generation.")
|
|
|
|
save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Saves the images as jpg instead of png.")
|
|
|
|
# check if GFPGAN, RealESRGAN and LDSR are available.
|
|
#if "GFPGAN_available" not in st.session_state:
|
|
GFPGAN_available()
|
|
|
|
#if "RealESRGAN_available" not in st.session_state:
|
|
RealESRGAN_available()
|
|
|
|
#if "LDSR_available" not in st.session_state:
|
|
LDSR_available()
|
|
|
|
if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
|
|
with st.expander("Post-Processing"):
|
|
face_restoration_tab, upscaling_tab = st.tabs(["Face Restoration", "Upscaling"])
|
|
with face_restoration_tab:
|
|
# GFPGAN used for face restoration
|
|
if st.session_state["GFPGAN_available"]:
|
|
#with st.expander("Face Restoration"):
|
|
#if st.session_state["GFPGAN_available"]:
|
|
#with st.expander("GFPGAN"):
|
|
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN,
|
|
help="Uses the GFPGAN model to improve faces after the generation.\
|
|
This greatly improve the quality and consistency of faces but uses\
|
|
extra VRAM. Disable if you need the extra VRAM.")
|
|
|
|
st.session_state["GFPGAN_model"] = st.selectbox("GFPGAN model", st.session_state["GFPGAN_models"],
|
|
index=st.session_state["GFPGAN_models"].index(st.session_state['defaults'].general.GFPGAN_model))
|
|
|
|
#st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')
|
|
|
|
else:
|
|
st.session_state["use_GFPGAN"] = False
|
|
|
|
with upscaling_tab:
|
|
st.session_state['use_upscaling'] = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2img.use_upscaling)
|
|
|
|
# RealESRGAN and LDSR used for upscaling.
|
|
if st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
|
|
|
|
upscaling_method_list = []
|
|
if st.session_state["RealESRGAN_available"]:
|
|
upscaling_method_list.append("RealESRGAN")
|
|
if st.session_state["LDSR_available"]:
|
|
upscaling_method_list.append("LDSR")
|
|
|
|
#print (st.session_state["RealESRGAN_available"])
|
|
st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
|
|
index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method)
|
|
if st.session_state['defaults'].general.upscaling_method in upscaling_method_list
|
|
else 0)
|
|
|
|
if st.session_state["RealESRGAN_available"]:
|
|
with st.expander("RealESRGAN"):
|
|
if st.session_state["upscaling_method"] == "RealESRGAN" and st.session_state['use_upscaling']:
|
|
st.session_state["use_RealESRGAN"] = True
|
|
else:
|
|
st.session_state["use_RealESRGAN"] = False
|
|
|
|
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", st.session_state["RealESRGAN_models"],
|
|
index=st.session_state["RealESRGAN_models"].index(st.session_state['defaults'].general.RealESRGAN_model))
|
|
else:
|
|
st.session_state["use_RealESRGAN"] = False
|
|
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
|
|
|
|
|
|
#
|
|
if st.session_state["LDSR_available"]:
|
|
with st.expander("LDSR"):
|
|
if st.session_state["upscaling_method"] == "LDSR" and st.session_state['use_upscaling']:
|
|
st.session_state["use_LDSR"] = True
|
|
else:
|
|
st.session_state["use_LDSR"] = False
|
|
|
|
st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
|
|
index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
|
|
|
|
st.session_state["ldsr_sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2img.LDSR_config.sampling_steps,
|
|
help="")
|
|
|
|
st.session_state["preDownScale"] = st.number_input("PreDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.preDownScale,
|
|
help="")
|
|
|
|
st.session_state["postDownScale"] = st.number_input("postDownScale", value=st.session_state['defaults'].txt2img.LDSR_config.postDownScale,
|
|
help="")
|
|
|
|
downsample_method_list = ['Nearest', 'Lanczos']
|
|
st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list,
|
|
index=downsample_method_list.index(st.session_state['defaults'].txt2img.LDSR_config.downsample_method))
|
|
|
|
else:
|
|
st.session_state["use_LDSR"] = False
|
|
st.session_state["LDSR_model"] = "model"
|
|
|
|
with st.expander("Variant"):
|
|
variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount.value,
|
|
min_value=st.session_state['defaults'].txt2img.variant_amount.min_value, max_value=st.session_state['defaults'].txt2img.variant_amount.max_value,
|
|
step=st.session_state['defaults'].txt2img.variant_amount.step)
|
|
variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed,
|
|
help="The seed to use when generating a variant, if left blank a random seed will be generated.")
|
|
|
|
#galleryCont = st.empty()
|
|
|
|
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
|
|
generate_col1.write("")
|
|
generate_col1.write("")
|
|
generate_button = generate_col1.form_submit_button("Generate")
|
|
|
|
#
|
|
if generate_button:
|
|
|
|
with col2:
|
|
if not use_stable_horde:
|
|
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
|
|
load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
|
|
use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,
|
|
use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
|
|
CustomModel_available=server_state["CustomModel_available"], custom_model=st.session_state["custom_model"])
|
|
|
|
#print(st.session_state['use_RealESRGAN'])
|
|
#print(st.session_state['use_LDSR'])
|
|
#try:
|
|
#
|
|
|
|
output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["batch_count"], st.session_state["batch_size"],
|
|
cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
|
|
save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state['GFPGAN_model'],
|
|
use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"],
|
|
use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
|
|
variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files,
|
|
use_stable_horde=use_stable_horde, stable_horde_key=stable_horde_key)
|
|
|
|
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
|
|
|
|
with gallery_tab:
|
|
logger.info(seeds)
|
|
st.session_state["gallery"].text = ""
|
|
sdGallery(output_images)
|
|
|
|
|
|
#except (StopException, KeyError):
|
|
#print(f"Received Streamlit StopException")
|
|
|
|
# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
|
|
# use the current col2 first tab to show the preview_img and update it as its generated.
|
|
#preview_image.image(output_images)
|
|
|
|
|