mirror of
https://github.com/sd-webui/stable-diffusion-webui.git
synced 2024-12-18 02:31:47 +03:00
7354c901d2
- Removed the checkbox to disable the preview image, instead users should increase the frequency at which it is displayed if they have performance issues, after a certain point it no longer affects performance.
2544 lines
112 KiB
Python
2544 lines
112 KiB
Python
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
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# Copyright 2022 sd-webui 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 webui_streamlit import st
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import hydralit as st
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# streamlit imports
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from streamlit import StopException, StreamlitAPIException
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#streamlit components section
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from streamlit_server_state import server_state, server_state_lock
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import hydralit_components as hc
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#other imports
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import warnings
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import json
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import base64
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import os, sys, re, random, datetime, time, math, glob, toml
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import gc
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from PIL import Image, ImageFont, ImageDraw, ImageFilter
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from PIL.PngImagePlugin import PngInfo
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from scipy import integrate
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import torch
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from torchdiffeq import odeint
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import k_diffusion as K
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import math, requests
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import mimetypes
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import numpy as np
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from numpy import asarray
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import pynvml
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import threading
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import torch, torchvision
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from torch import autocast
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from torchvision import transforms
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import torch.nn as nn
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from omegaconf import OmegaConf
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import yaml
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from pathlib import Path
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from contextlib import nullcontext
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from einops import rearrange, repeat
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from ldm.util import instantiate_from_config
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from retry import retry
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from slugify import slugify
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import skimage
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import piexif
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import piexif.helper
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from tqdm import trange
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.util import ismap
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# Temp imports
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#from basicsr.utils.registry import ARCH_REGISTRY
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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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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# remove some annoying deprecation warnings that show every now and then.
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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if not "defaults" in st.session_state:
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st.session_state["defaults"] = {}
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st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")
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if (os.path.exists("configs/webui/userconfig_streamlit.yaml")):
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user_defaults = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
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st.session_state["defaults"] = OmegaConf.merge(st.session_state["defaults"], user_defaults)
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else:
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OmegaConf.save(config=st.session_state.defaults, f="configs/webui/userconfig_streamlit.yaml")
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loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
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assert st.session_state.defaults == loaded
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if (os.path.exists(".streamlit/config.toml")):
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st.session_state["streamlit_config"] = toml.load(".streamlit/config.toml")
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if st.session_state["defaults"].daisi_app.running_on_daisi_io:
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if os.path.exists("scripts/modeldownload.py"):
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import modeldownload
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modeldownload.updateModels()
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#
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#app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", sidebar_state="expanded",
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#hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False)
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# should and will be moved to a settings menu in the UI at some point
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grid_format = [s.lower() for s in st.session_state["defaults"].general.grid_format.split(':')]
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grid_lossless = False
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grid_quality = 100
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if grid_format[0] == 'png':
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grid_ext = 'png'
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grid_format = 'png'
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elif grid_format[0] in ['jpg', 'jpeg']:
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grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
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grid_ext = 'jpg'
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grid_format = 'jpeg'
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elif grid_format[0] == 'webp':
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grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
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grid_ext = 'webp'
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grid_format = 'webp'
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if grid_quality < 0: # e.g. webp:-100 for lossless mode
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grid_lossless = True
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grid_quality = abs(grid_quality)
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# should and will be moved to a settings menu in the UI at some point
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save_format = [s.lower() for s in st.session_state["defaults"].general.save_format.split(':')]
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save_lossless = False
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save_quality = 100
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if save_format[0] == 'png':
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save_ext = 'png'
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save_format = 'png'
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elif save_format[0] in ['jpg', 'jpeg']:
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save_quality = int(save_format[1]) if len(save_format) > 1 else 100
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save_ext = 'jpg'
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save_format = 'jpeg'
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elif save_format[0] == 'webp':
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save_quality = int(save_format[1]) if len(save_format) > 1 else 100
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save_ext = 'webp'
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save_format = 'webp'
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if save_quality < 0: # e.g. webp:-100 for lossless mode
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save_lossless = True
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save_quality = abs(save_quality)
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# this should force GFPGAN and RealESRGAN onto the selected gpu as well
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
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os.environ["CUDA_VISIBLE_DEVICES"] = str(st.session_state["defaults"].general.gpu)
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#
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# functions to load css locally OR remotely starts here. Options exist for future flexibility. Called as st.markdown with unsafe_allow_html as css injection
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# TODO, maybe look into async loading the file especially for remote fetching
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def local_css(file_name):
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with open(file_name) as f:
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st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
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def remote_css(url):
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st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True)
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def load_css(isLocal, nameOrURL):
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if(isLocal):
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local_css(nameOrURL)
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else:
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remote_css(nameOrURL)
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def set_page_title(title):
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"""
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Simple function to allows us to change the title dynamically.
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Normally you can use `st.set_page_config` to change the title but it can only be used once per app.
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"""
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st.sidebar.markdown(unsafe_allow_html=True, body=f"""
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<iframe height=0 srcdoc="<script>
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const title = window.parent.document.querySelector('title') \
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const oldObserver = window.parent.titleObserver
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if (oldObserver) {{
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oldObserver.disconnect()
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}} \
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const newObserver = new MutationObserver(function(mutations) {{
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const target = mutations[0].target
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if (target.text !== '{title}') {{
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target.text = '{title}'
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}}
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}}) \
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newObserver.observe(title, {{ childList: true }})
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window.parent.titleObserver = newObserver \
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title.text = '{title}'
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</script>" />
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""")
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def human_readable_size(size, decimal_places=3):
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"""Return a human readable size from bytes."""
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for unit in ['B','KB','MB','GB','TB']:
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if size < 1024.0:
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break
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size /= 1024.0
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return f"{size:.{decimal_places}f}{unit}"
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def load_models(use_LDSR = False, LDSR_model='model', use_GFPGAN=False, GFPGAN_model='GFPGANv1.3', use_RealESRGAN=False, RealESRGAN_model="RealESRGAN_x4plus",
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CustomModel_available=False, custom_model="Stable Diffusion v1.4"):
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"""Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again. """
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print ("Loading models.")
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if "progress_bar_text" in st.session_state:
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st.session_state["progress_bar_text"].text("")
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# Generate random run ID
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# Used to link runs linked w/ continue_prev_run which is not yet implemented
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# Use URL and filesystem safe version just in case.
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st.session_state["run_id"] = base64.urlsafe_b64encode(
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os.urandom(6)
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).decode("ascii")
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# check what models we want to use and if the they are already loaded.
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with server_state_lock["LDSR"]:
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if use_LDSR:
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if "LDSR" in server_state and server_state["LDSR"].name == LDSR_model:
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print("LDSR already loaded")
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else:
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if "LDSR" in server_state:
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del server_state["LDSR"]
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# Load GFPGAN
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if os.path.exists(st.session_state["defaults"].general.LDSR_dir):
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try:
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server_state["LDSR"] = load_LDSR(model_name=LDSR_model)
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print(f"Loaded LDSR")
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except Exception:
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import traceback
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print(f"Error loading LDSR:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if "LDSR" in server_state:
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del server_state["LDSR"]
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with server_state_lock["GFPGAN"]:
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if use_GFPGAN:
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if "GFPGAN" in server_state and server_state["GFPGAN"].name == GFPGAN_model:
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print("GFPGAN already loaded")
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else:
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if "GFPGAN" in server_state:
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del server_state["GFPGAN"]
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# Load GFPGAN
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if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
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try:
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server_state["GFPGAN"] = load_GFPGAN(GFPGAN_model)
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print(f"Loaded GFPGAN: {GFPGAN_model}")
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except Exception:
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import traceback
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print(f"Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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else:
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if "GFPGAN" in server_state:
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del server_state["GFPGAN"]
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with server_state_lock["RealESRGAN"]:
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if use_RealESRGAN:
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if "RealESRGAN" in server_state and server_state["RealESRGAN"].model.name == RealESRGAN_model:
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print("RealESRGAN already loaded")
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else:
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#Load RealESRGAN
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try:
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# We first remove the variable in case it has something there,
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# some errors can load the model incorrectly and leave things in memory.
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del server_state["RealESRGAN"]
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except KeyError:
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pass
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if os.path.exists(st.session_state["defaults"].general.RealESRGAN_dir):
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# st.session_state is used for keeping the models in memory across multiple pages or runs.
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server_state["RealESRGAN"] = load_RealESRGAN(RealESRGAN_model)
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print("Loaded RealESRGAN with model "+ server_state["RealESRGAN"].model.name)
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else:
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if "RealESRGAN" in server_state:
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del server_state["RealESRGAN"]
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with server_state_lock["model"], server_state_lock["modelCS"], server_state_lock["modelFS"], server_state_lock["loaded_model"]:
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if "model" in server_state:
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if "model" in server_state and server_state["loaded_model"] == custom_model:
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# TODO: check if the optimized mode was changed?
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print("Model already loaded")
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return
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else:
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try:
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del server_state["model"]
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del server_state["modelCS"]
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del server_state["modelFS"]
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del server_state["loaded_model"]
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except KeyError:
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pass
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# if the model from txt2vid is in memory we need to remove it to improve performance.
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with server_state_lock["pipe"]:
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if "pipe" in server_state:
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del server_state["pipe"]
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if "textual_inversion" in st.session_state:
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del st.session_state['textual_inversion']
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# At this point the model is either
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# not loaded yet or have been evicted:
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# load new model into memory
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server_state["custom_model"] = custom_model
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config, device, model, modelCS, modelFS = load_sd_model(custom_model)
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server_state["device"] = device
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server_state["model"] = model
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server_state["modelCS"] = modelCS
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server_state["modelFS"] = modelFS
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server_state["loaded_model"] = custom_model
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#trying to disable multiprocessing as it makes it so streamlit cant stop when the
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# model is loaded in memory and you need to kill the process sometimes.
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server_state["model"].args.use_multiprocessing_for_evaluation = False
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if st.session_state.defaults.general.enable_attention_slicing:
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server_state["model"].enable_attention_slicing()
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if st.session_state.defaults.general.enable_minimal_memory_usage:
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server_state["model"].enable_minimal_memory_usage()
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print("Model loaded.")
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return True
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def load_model_from_config(config, ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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if len(m) > 0 and verbose:
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print("missing keys:")
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print(m)
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if len(u) > 0 and verbose:
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print("unexpected keys:")
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print(u)
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model.cuda()
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model.eval()
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return model
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def load_sd_from_config(ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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return sd
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class MemUsageMonitor(threading.Thread):
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stop_flag = False
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max_usage = 0
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total = -1
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def __init__(self, name):
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threading.Thread.__init__(self)
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self.name = name
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def run(self):
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try:
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pynvml.nvmlInit()
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except:
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print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
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return
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print(f"[{self.name}] Recording max memory usage...\n")
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# Missing context
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#handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
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while not self.stop_flag:
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m = pynvml.nvmlDeviceGetMemoryInfo(handle)
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self.max_usage = max(self.max_usage, m.used)
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# print(self.max_usage)
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time.sleep(0.1)
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print(f"[{self.name}] Stopped recording.\n")
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pynvml.nvmlShutdown()
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def read(self):
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return self.max_usage, self.total
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def stop(self):
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self.stop_flag = True
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def read_and_stop(self):
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self.stop_flag = True
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return self.max_usage, self.total
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class CFGMaskedDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
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x_in = x
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x_in = torch.cat([x_in] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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denoised = uncond + (cond - uncond) * cond_scale
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if mask is not None:
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assert x0 is not None
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img_orig = x0
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mask_inv = 1. - mask
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denoised = (img_orig * mask_inv) + (mask * denoised)
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return denoised
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class CFGDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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return uncond + (cond - uncond) * cond_scale
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def append_zero(x):
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return torch.cat([x, x.new_zeros([1])])
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
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return x[(...,) + (None,) * dims_to_append]
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def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
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"""Constructs the noise schedule of Karras et al. (2022)."""
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ramp = torch.linspace(0, 1, n)
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min_inv_rho = sigma_min ** (1 / rho)
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max_inv_rho = sigma_max ** (1 / rho)
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sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
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return append_zero(sigmas).to(device)
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#
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# helper fft routines that keep ortho normalization and auto-shift before and after fft
|
||
def _fft2(data):
|
||
if data.ndim > 2: # has channels
|
||
out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
|
||
for c in range(data.shape[2]):
|
||
c_data = data[:,:,c]
|
||
out_fft[:,:,c] = np.fft.fft2(np.fft.fftshift(c_data),norm="ortho")
|
||
out_fft[:,:,c] = np.fft.ifftshift(out_fft[:,:,c])
|
||
else: # one channel
|
||
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||
out_fft[:,:] = np.fft.fft2(np.fft.fftshift(data),norm="ortho")
|
||
out_fft[:,:] = np.fft.ifftshift(out_fft[:,:])
|
||
|
||
return out_fft
|
||
|
||
def _ifft2(data):
|
||
if data.ndim > 2: # has channels
|
||
out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
|
||
for c in range(data.shape[2]):
|
||
c_data = data[:,:,c]
|
||
out_ifft[:,:,c] = np.fft.ifft2(np.fft.fftshift(c_data),norm="ortho")
|
||
out_ifft[:,:,c] = np.fft.ifftshift(out_ifft[:,:,c])
|
||
else: # one channel
|
||
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||
out_ifft[:,:] = np.fft.ifft2(np.fft.fftshift(data),norm="ortho")
|
||
out_ifft[:,:] = np.fft.ifftshift(out_ifft[:,:])
|
||
|
||
return out_ifft
|
||
|
||
def _get_gaussian_window(width, height, std=3.14, mode=0):
|
||
|
||
window_scale_x = float(width / min(width, height))
|
||
window_scale_y = float(height / min(width, height))
|
||
|
||
window = np.zeros((width, height))
|
||
x = (np.arange(width) / width * 2. - 1.) * window_scale_x
|
||
for y in range(height):
|
||
fy = (y / height * 2. - 1.) * window_scale_y
|
||
if mode == 0:
|
||
window[:, y] = np.exp(-(x**2+fy**2) * std)
|
||
else:
|
||
window[:, y] = (1/((x**2+1.) * (fy**2+1.))) ** (std/3.14) # hey wait a minute that's not gaussian
|
||
|
||
return window
|
||
|
||
def _get_masked_window_rgb(np_mask_grey, hardness=1.):
|
||
np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
|
||
if hardness != 1.:
|
||
hardened = np_mask_grey[:] ** hardness
|
||
else:
|
||
hardened = np_mask_grey[:]
|
||
for c in range(3):
|
||
np_mask_rgb[:,:,c] = hardened[:]
|
||
return np_mask_rgb
|
||
|
||
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q, color_variation):
|
||
"""
|
||
Explanation:
|
||
Getting good results in/out-painting with stable diffusion can be challenging.
|
||
Although there are simpler effective solutions for in-painting, out-painting can be especially challenging because there is no color data
|
||
in the masked area to help prompt the generator. Ideally, even for in-painting we'd like work effectively without that data as well.
|
||
Provided here is my take on a potential solution to this problem.
|
||
|
||
By taking a fourier transform of the masked src img we get a function that tells us the presence and orientation of each feature scale in the unmasked src.
|
||
Shaping the init/seed noise for in/outpainting to the same distribution of feature scales, orientations, and positions increases output coherence
|
||
by helping keep features aligned. This technique is applicable to any continuous generation task such as audio or video, each of which can
|
||
be conceptualized as a series of out-painting steps where the last half of the input "frame" is erased. For multi-channel data such as color
|
||
or stereo sound the "color tone" or histogram of the seed noise can be matched to improve quality (using scikit-image currently)
|
||
This method is quite robust and has the added benefit of being fast independently of the size of the out-painted area.
|
||
The effects of this method include things like helping the generator integrate the pre-existing view distance and camera angle.
|
||
|
||
Carefully managing color and brightness with histogram matching is also essential to achieving good coherence.
|
||
|
||
noise_q controls the exponent in the fall-off of the distribution can be any positive number, lower values means higher detail (range > 0, default 1.)
|
||
color_variation controls how much freedom is allowed for the colors/palette of the out-painted area (range 0..1, default 0.01)
|
||
This code is provided as is under the Unlicense (https://unlicense.org/)
|
||
Although you have no obligation to do so, if you found this code helpful please find it in your heart to credit me [parlance-zz].
|
||
|
||
Questions or comments can be sent to parlance@fifth-harmonic.com (https://github.com/parlance-zz/)
|
||
This code is part of a new branch of a discord bot I am working on integrating with diffusers (https://github.com/parlance-zz/g-diffuser-bot)
|
||
|
||
"""
|
||
|
||
global DEBUG_MODE
|
||
global TMP_ROOT_PATH
|
||
|
||
width = _np_src_image.shape[0]
|
||
height = _np_src_image.shape[1]
|
||
num_channels = _np_src_image.shape[2]
|
||
|
||
np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
|
||
np_mask_grey = (np.sum(np_mask_rgb, axis=2)/3.)
|
||
np_src_grey = (np.sum(np_src_image, axis=2)/3.)
|
||
all_mask = np.ones((width, height), dtype=bool)
|
||
img_mask = np_mask_grey > 1e-6
|
||
ref_mask = np_mask_grey < 1e-3
|
||
|
||
windowed_image = _np_src_image * (1.-_get_masked_window_rgb(np_mask_grey))
|
||
windowed_image /= np.max(windowed_image)
|
||
windowed_image += np.average(_np_src_image) * np_mask_rgb# / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
|
||
#windowed_image += np.average(_np_src_image) * (np_mask_rgb * (1.- np_mask_rgb)) / (1.-np.average(np_mask_rgb)) # compensate for darkening across the mask transition area
|
||
#_save_debug_img(windowed_image, "windowed_src_img")
|
||
|
||
src_fft = _fft2(windowed_image) # get feature statistics from masked src img
|
||
src_dist = np.absolute(src_fft)
|
||
src_phase = src_fft / src_dist
|
||
#_save_debug_img(src_dist, "windowed_src_dist")
|
||
|
||
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
|
||
noise_rgb = np.random.random_sample((width, height, num_channels))
|
||
noise_grey = (np.sum(noise_rgb, axis=2)/3.)
|
||
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
|
||
for c in range(num_channels):
|
||
noise_rgb[:,:,c] += (1. - color_variation) * noise_grey
|
||
|
||
noise_fft = _fft2(noise_rgb)
|
||
for c in range(num_channels):
|
||
noise_fft[:,:,c] *= noise_window
|
||
noise_rgb = np.real(_ifft2(noise_fft))
|
||
shaped_noise_fft = _fft2(noise_rgb)
|
||
shaped_noise_fft[:,:,:] = np.absolute(shaped_noise_fft[:,:,:])**2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
|
||
|
||
brightness_variation = 0.#color_variation # todo: temporarily tieing brightness variation to color variation for now
|
||
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
|
||
|
||
# scikit-image is used for histogram matching, very convenient!
|
||
shaped_noise = np.real(_ifft2(shaped_noise_fft))
|
||
shaped_noise -= np.min(shaped_noise)
|
||
shaped_noise /= np.max(shaped_noise)
|
||
shaped_noise[img_mask,:] = skimage.exposure.match_histograms(shaped_noise[img_mask,:]**1., contrast_adjusted_np_src[ref_mask,:], channel_axis=1)
|
||
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
|
||
#_save_debug_img(shaped_noise, "shaped_noise")
|
||
|
||
matched_noise = np.zeros((width, height, num_channels))
|
||
matched_noise = shaped_noise[:]
|
||
#matched_noise[all_mask,:] = skimage.exposure.match_histograms(shaped_noise[all_mask,:], _np_src_image[ref_mask,:], channel_axis=1)
|
||
#matched_noise = _np_src_image[:] * (1. - np_mask_rgb) + matched_noise * np_mask_rgb
|
||
|
||
#_save_debug_img(matched_noise, "matched_noise")
|
||
|
||
"""
|
||
todo:
|
||
color_variation doesnt have to be a single number, the overall color tone of the out-painted area could be param controlled
|
||
"""
|
||
|
||
return np.clip(matched_noise, 0., 1.)
|
||
|
||
|
||
#
|
||
def find_noise_for_image(model, device, init_image, prompt, steps=200, cond_scale=2.0, verbose=False, normalize=False, generation_callback=None):
|
||
image = np.array(init_image).astype(np.float32) / 255.0
|
||
image = image[None].transpose(0, 3, 1, 2)
|
||
image = torch.from_numpy(image)
|
||
image = 2. * image - 1.
|
||
image = image.to(device)
|
||
x = model.get_first_stage_encoding(model.encode_first_stage(image))
|
||
|
||
uncond = model.get_learned_conditioning([''])
|
||
cond = model.get_learned_conditioning([prompt])
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
dnw = K.external.CompVisDenoiser(model)
|
||
sigmas = dnw.get_sigmas(steps).flip(0)
|
||
|
||
if verbose:
|
||
print(sigmas)
|
||
|
||
for i in trange(1, len(sigmas)):
|
||
x_in = torch.cat([x] * 2)
|
||
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
|
||
cond_in = torch.cat([uncond, cond])
|
||
|
||
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
|
||
|
||
if i == 1:
|
||
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
|
||
else:
|
||
t = dnw.sigma_to_t(sigma_in)
|
||
|
||
eps = model.apply_model(x_in * c_in, t, cond=cond_in)
|
||
denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
|
||
|
||
denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cond_scale
|
||
|
||
if i == 1:
|
||
d = (x - denoised) / (2 * sigmas[i])
|
||
else:
|
||
d = (x - denoised) / sigmas[i - 1]
|
||
|
||
if generation_callback is not None:
|
||
generation_callback(x, i)
|
||
|
||
dt = sigmas[i] - sigmas[i - 1]
|
||
x = x + d * dt
|
||
|
||
return x / sigmas[-1]
|
||
|
||
#
|
||
def folder_picker(label="Select:", value="", help="", folder_button_label="Select", folder_button_help="", folder_button_key=""):
|
||
"""A folder picker that has a text_input field next to it and a button to select the folder.
|
||
Returns the text_input field with the folder path."""
|
||
import tkinter as tk
|
||
from tkinter import filedialog
|
||
import string
|
||
|
||
# Set up tkinter
|
||
root = tk.Tk()
|
||
root.withdraw()
|
||
|
||
# Make folder picker dialog appear on top of other windows
|
||
root.wm_attributes('-topmost', 1)
|
||
|
||
col1, col2 = st.columns([2,1], gap="small")
|
||
|
||
with col1:
|
||
dirname = st.empty()
|
||
with col2:
|
||
st.write("")
|
||
st.write("")
|
||
folder_picker = st.empty()
|
||
|
||
# Folder picker button
|
||
#st.title('Folder Picker')
|
||
#st.write('Please select a folder:')
|
||
|
||
# Create a label and add a random number of invisible characters
|
||
# to it so no two buttons inside a form are the same.
|
||
#folder_button_label = ''.join(random.choice(f"{folder_button_label}") for _ in range(5))
|
||
folder_button_label = f"{str(folder_button_label)}{'' * random.randint(1, 500)}"
|
||
clicked = folder_button_key + '' * random.randint(5, 500)
|
||
|
||
#try:
|
||
#clicked = folder_picker.button(folder_button_label, help=folder_button_help, key=folder_button_key)
|
||
#except StreamlitAPIException:
|
||
clicked = folder_picker.form_submit_button(folder_button_label, help=folder_button_help)
|
||
|
||
if clicked:
|
||
dirname = dirname.text_input(label, filedialog.askdirectory(master=root), help=help)
|
||
else:
|
||
dirname = dirname.text_input(label, value, help=help)
|
||
|
||
return dirname
|
||
|
||
|
||
def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
|
||
"""Constructs an exponential noise schedule."""
|
||
sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
|
||
return append_zero(sigmas)
|
||
|
||
|
||
def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
|
||
"""Constructs a continuous VP noise schedule."""
|
||
t = torch.linspace(1, eps_s, n, device=device)
|
||
sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
|
||
return append_zero(sigmas)
|
||
|
||
|
||
def to_d(x, sigma, denoised):
|
||
"""Converts a denoiser output to a Karras ODE derivative."""
|
||
return (x - denoised) / append_dims(sigma, x.ndim)
|
||
|
||
def linear_multistep_coeff(order, t, i, j):
|
||
if order - 1 > i:
|
||
raise ValueError(f'Order {order} too high for step {i}')
|
||
def fn(tau):
|
||
prod = 1.
|
||
for k in range(order):
|
||
if j == k:
|
||
continue
|
||
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
|
||
return prod
|
||
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
|
||
|
||
class KDiffusionSampler:
|
||
def __init__(self, m, sampler):
|
||
self.model = m
|
||
self.model_wrap = K.external.CompVisDenoiser(m)
|
||
self.schedule = sampler
|
||
def get_sampler_name(self):
|
||
return self.schedule
|
||
def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T, img_callback=None, log_every_t=None):
|
||
sigmas = self.model_wrap.get_sigmas(S)
|
||
x = x_T * sigmas[0]
|
||
model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
||
samples_ddim = None
|
||
samples_ddim = K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas,
|
||
extra_args={'cond': conditioning, 'uncond': unconditional_conditioning,
|
||
'cond_scale': unconditional_guidance_scale}, disable=False, callback=generation_callback)
|
||
#
|
||
return samples_ddim, None
|
||
#
|
||
#create class LDSR
|
||
class LDSR():
|
||
#init function
|
||
def __init__(self, modelPath,yamlPath):
|
||
self.modelPath = modelPath
|
||
self.yamlPath = yamlPath
|
||
#self.model = self.load_model_from_config()
|
||
#print(self.load_model_from_config(OmegaConf.load(yamlPath), modelPath))
|
||
#self.print_current_directory()
|
||
#get currennt directory
|
||
|
||
'''
|
||
def check_model_exists(self):
|
||
#check if model and yaml exist
|
||
path = self.pathInput + "/models/ldm/ld_sr".replace('\\',os.sep).replace('/',os.sep)
|
||
model = self.modelName
|
||
yaml = self.yamlName
|
||
if os.path.exists(path):
|
||
#check if yaml exists
|
||
if os.path.exists(os.path.join(path,yaml)):
|
||
print('YAML found')
|
||
#check if ckpt exists
|
||
if os.path.exists(os.path.join(path,model)):
|
||
print('Model found')
|
||
return os.path.join(path,model), os.path.join(path,yaml)
|
||
else:
|
||
return False
|
||
#return onlyfiles
|
||
'''
|
||
def load_model_from_config(self):
|
||
#print(f"Loading model from {self.modelPath}")
|
||
pl_sd = torch.load(self.modelPath, map_location="cpu")
|
||
global_step = pl_sd["global_step"]
|
||
sd = pl_sd["state_dict"]
|
||
config = OmegaConf.load(self.yamlPath)
|
||
model = instantiate_from_config(config.model)
|
||
m, u = model.load_state_dict(sd, strict=False)
|
||
model.cuda()
|
||
model.eval()
|
||
return {"model": model}#, global_step
|
||
|
||
'''
|
||
def get_model(self):
|
||
check = self.check_model_exists()
|
||
if check != False:
|
||
path_ckpt = check[0]
|
||
path_conf = check[1]
|
||
else:
|
||
print('Model not found, please run the bat file to download the model')
|
||
config = OmegaConf.load(path_conf)
|
||
model, step = self.load_model_from_config(config, path_ckpt)
|
||
return model
|
||
|
||
|
||
def get_custom_cond(mode):
|
||
dest = "data/example_conditioning"
|
||
|
||
if mode == "superresolution":
|
||
uploaded_img = files.upload()
|
||
filename = next(iter(uploaded_img))
|
||
name, filetype = filename.split(".") # todo assumes just one dot in name !
|
||
os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
|
||
|
||
elif mode == "text_conditional":
|
||
#w = widgets.Text(value='A cake with cream!', disabled=True)
|
||
w = 'Empty Test'
|
||
display.display(w)
|
||
|
||
with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
|
||
f.write(w.value)
|
||
|
||
elif mode == "class_conditional":
|
||
#w = widgets.IntSlider(min=0, max=1000)
|
||
w = 1000
|
||
display.display(w)
|
||
with open(f"{dest}/{mode}/custom.txt", 'w') as f:
|
||
f.write(w.value)
|
||
|
||
else:
|
||
raise NotImplementedError(f"cond not implemented for mode{mode}")
|
||
'''
|
||
|
||
def get_cond_options(self,mode):
|
||
path = "data/example_conditioning"
|
||
path = os.path.join(path, mode)
|
||
onlyfiles = [f for f in sorted(os.listdir(path))]
|
||
return path, onlyfiles
|
||
|
||
'''
|
||
def select_cond_path(mode):
|
||
path = "data/example_conditioning" # todo
|
||
path = os.path.join(path, mode)
|
||
onlyfiles = [f for f in sorted(os.listdir(path))]
|
||
|
||
selected = widgets.RadioButtons(
|
||
options=onlyfiles,
|
||
description='Select conditioning:',
|
||
disabled=False
|
||
)
|
||
display.display(selected)
|
||
selected_path = os.path.join(path, selected.value)
|
||
return selected_path
|
||
'''
|
||
|
||
|
||
|
||
'''
|
||
# Google Collab stuff
|
||
def visualize_cond_img(path):
|
||
display.display(ipyimg(filename=path))
|
||
'''
|
||
|
||
def run(self,model, selected_path, task, custom_steps, eta, resize_enabled=False, classifier_ckpt=None, global_step=None):
|
||
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
|
||
invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
|
||
resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
|
||
corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
|
||
log = dict()
|
||
|
||
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
|
||
return_first_stage_outputs=True,
|
||
force_c_encode=not (hasattr(model, 'split_input_params')
|
||
and model.cond_stage_key == 'coordinates_bbox'),
|
||
return_original_cond=True)
|
||
|
||
log_every_t = 1 if save_intermediate_vid else None
|
||
|
||
if custom_shape is not None:
|
||
z = torch.randn(custom_shape)
|
||
# print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
|
||
|
||
z0 = None
|
||
|
||
log["input"] = x
|
||
log["reconstruction"] = xrec
|
||
|
||
if ismap(xc):
|
||
log["original_conditioning"] = model.to_rgb(xc)
|
||
if hasattr(model, 'cond_stage_key'):
|
||
log[model.cond_stage_key] = model.to_rgb(xc)
|
||
|
||
else:
|
||
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
|
||
if model.cond_stage_model:
|
||
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
|
||
if model.cond_stage_key =='class_label':
|
||
log[model.cond_stage_key] = xc[model.cond_stage_key]
|
||
|
||
with model.ema_scope("Plotting"):
|
||
t0 = time.time()
|
||
img_cb = None
|
||
|
||
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
|
||
eta=eta,
|
||
quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
|
||
temperature=temperature, noise_dropout=noise_dropout,
|
||
score_corrector=corrector, corrector_kwargs=corrector_kwargs,
|
||
x_T=x_T, log_every_t=log_every_t)
|
||
t1 = time.time()
|
||
|
||
if ddim_use_x0_pred:
|
||
sample = intermediates['pred_x0'][-1]
|
||
|
||
x_sample = model.decode_first_stage(sample)
|
||
|
||
try:
|
||
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
|
||
log["sample_noquant"] = x_sample_noquant
|
||
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
|
||
except:
|
||
pass
|
||
|
||
log["sample"] = x_sample
|
||
log["time"] = t1 - t0
|
||
|
||
return log
|
||
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
|
||
mask=None, x0=None, quantize_x0=False, img_callback=None,
|
||
temperature=1., noise_dropout=0., score_corrector=None,
|
||
corrector_kwargs=None, x_T=None, log_every_t=None
|
||
):
|
||
|
||
ddim = DDIMSampler(model)
|
||
bs = shape[0] # dont know where this comes from but wayne
|
||
shape = shape[1:] # cut batch dim
|
||
print(f"Sampling with eta = {eta}; steps: {steps}")
|
||
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
|
||
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
|
||
mask=mask, x0=x0, temperature=temperature, verbose=False,
|
||
score_corrector=score_corrector,
|
||
corrector_kwargs=corrector_kwargs, x_T=x_T)
|
||
|
||
return samples, intermediates
|
||
# global stride
|
||
def get_cond(mode, selected_path):
|
||
example = dict()
|
||
if mode == "superresolution":
|
||
up_f = 4
|
||
#visualize_cond_img(selected_path)
|
||
|
||
c = selected_path.convert('RGB')
|
||
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
|
||
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
|
||
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
|
||
c = rearrange(c, '1 c h w -> 1 h w c')
|
||
c = 2. * c - 1.
|
||
|
||
c = c.to(torch.device("cuda"))
|
||
example["LR_image"] = c
|
||
example["image"] = c_up
|
||
|
||
return example
|
||
example = get_cond(task, selected_path)
|
||
|
||
save_intermediate_vid = False
|
||
n_runs = 1
|
||
masked = False
|
||
guider = None
|
||
ckwargs = None
|
||
mode = 'ddim'
|
||
ddim_use_x0_pred = False
|
||
temperature = 1.
|
||
eta = eta
|
||
make_progrow = True
|
||
custom_shape = None
|
||
|
||
height, width = example["image"].shape[1:3]
|
||
split_input = height >= 128 and width >= 128
|
||
|
||
if split_input:
|
||
ks = 128
|
||
stride = 64
|
||
vqf = 4 #
|
||
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
|
||
"vqf": vqf,
|
||
"patch_distributed_vq": True,
|
||
"tie_braker": False,
|
||
"clip_max_weight": 0.5,
|
||
"clip_min_weight": 0.01,
|
||
"clip_max_tie_weight": 0.5,
|
||
"clip_min_tie_weight": 0.01}
|
||
else:
|
||
if hasattr(model, "split_input_params"):
|
||
delattr(model, "split_input_params")
|
||
|
||
invert_mask = False
|
||
|
||
x_T = None
|
||
for n in range(n_runs):
|
||
if custom_shape is not None:
|
||
x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
|
||
x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
|
||
|
||
logs = make_convolutional_sample(example, model,
|
||
mode=mode, custom_steps=custom_steps,
|
||
eta=eta, swap_mode=False , masked=masked,
|
||
invert_mask=invert_mask, quantize_x0=False,
|
||
custom_schedule=None, decode_interval=10,
|
||
resize_enabled=resize_enabled, custom_shape=custom_shape,
|
||
temperature=temperature, noise_dropout=0.,
|
||
corrector=guider, corrector_kwargs=ckwargs, x_T=x_T, save_intermediate_vid=save_intermediate_vid,
|
||
make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
|
||
)
|
||
return logs
|
||
|
||
|
||
@torch.no_grad()
|
||
|
||
|
||
|
||
@torch.no_grad()
|
||
|
||
def superResolution(self,image,ddimSteps=100,preDownScale='None',postDownScale='None'):
|
||
diffMode = 'superresolution'
|
||
model = self.load_model_from_config()
|
||
#@title Import location
|
||
#@markdown ***File height and width should be multiples of 64, or image will be padded.***
|
||
|
||
#@markdown *To change upload settings without adding more, run and cancel upload*
|
||
#import_method = 'Directory' #@param ['Google Drive', 'Upload']
|
||
#output_subfolder_name = 'processed' #@param {type: 'string'}
|
||
|
||
#@markdown Drive method options:
|
||
#drive_directory = '/content/drive/MyDrive/upscaleTest' #@param {type: 'string'}
|
||
|
||
#@markdown Upload method options:
|
||
#remove_previous_uploads = False #@param {type: 'boolean'}
|
||
#save_output_to_drive = False #@param {type: 'boolean'}
|
||
#zip_if_not_drive = False #@param {type: 'boolean'}
|
||
'''
|
||
os.makedirs(pathInput+'/content/input'.replace('\\',os.sep).replace('/',os.sep), exist_ok=True)
|
||
output_directory = os.getcwd()+f'/content/output/{output_subfolder_name}'.replace('\\',os.sep).replace('/',os.sep)
|
||
os.makedirs(output_directory, exist_ok=True)
|
||
uploaded_img = pathInput+'/content/input/'.replace('\\',os.sep).replace('/',os.sep)
|
||
pathInput, dirsInput, filesInput = next(os.walk(pathInput+'/content/input').replace('\\',os.sep).replace('/',os.sep))
|
||
file_count = len(filesInput)
|
||
print(f'Found {file_count} files total')
|
||
'''
|
||
|
||
|
||
#Run settings
|
||
|
||
diffusion_steps = int(ddimSteps) #@param [25, 50, 100, 250, 500, 1000]
|
||
eta = 1.0 #@param {type: 'raw'}
|
||
stride = 0 #not working atm
|
||
|
||
# ####Scaling options:
|
||
# Downsampling to 256px first will often improve the final image and runs faster.
|
||
|
||
# You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)
|
||
pre_downsample = preDownScale #@param ['None', '1/2', '1/4']
|
||
|
||
post_downsample = postDownScale #@param ['None', 'Original Size', '1/2', '1/4']
|
||
|
||
# Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
|
||
downsample_method = 'Lanczos' #@param ['Nearest', 'Lanczos']
|
||
|
||
|
||
overwrite_prior_runs = True #@param {type: 'boolean'}
|
||
|
||
#pathProcessed, dirsProcessed, filesProcessed = next(os.walk(output_directory))
|
||
|
||
#for img in filesInput:
|
||
# if img in filesProcessed and overwrite_prior_runs is False:
|
||
# print(f'Skipping {img}: Already processed')
|
||
# continue
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
#dir = pathInput
|
||
#filepath = os.path.join(dir, img).replace('\\',os.sep).replace('/',os.sep)
|
||
|
||
im_og = image
|
||
width_og, height_og = im_og.size
|
||
|
||
#Downsample Pre
|
||
if pre_downsample == '1/2':
|
||
downsample_rate = 2
|
||
elif pre_downsample == '1/4':
|
||
downsample_rate = 4
|
||
else:
|
||
downsample_rate = 1
|
||
# get system temp directory
|
||
#dir = tempfile.gettempdir()
|
||
width_downsampled_pre = width_og//downsample_rate
|
||
height_downsampled_pre = height_og//downsample_rate
|
||
if downsample_rate != 1:
|
||
print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
|
||
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
|
||
#os.makedirs(dir, exist_ok=True)
|
||
#im_og.save(dir + '/ldsr/temp.png'.replace('\\',os.sep).replace('/',os.sep))
|
||
#filepath = dir + '/ldsr/temp.png'.replace('\\',os.sep).replace('/',os.sep)
|
||
|
||
logs = self.run(model["model"], im_og, diffMode, diffusion_steps, eta)
|
||
|
||
sample = logs["sample"]
|
||
sample = sample.detach().cpu()
|
||
sample = torch.clamp(sample, -1., 1.)
|
||
sample = (sample + 1.) / 2. * 255
|
||
sample = sample.numpy().astype(np.uint8)
|
||
sample = np.transpose(sample, (0, 2, 3, 1))
|
||
#print(sample.shape)
|
||
a = Image.fromarray(sample[0])
|
||
|
||
#Downsample Post
|
||
if post_downsample == '1/2':
|
||
downsample_rate = 2
|
||
elif post_downsample == '1/4':
|
||
downsample_rate = 4
|
||
else:
|
||
downsample_rate = 1
|
||
|
||
width, height = a.size
|
||
width_downsampled_post = width//downsample_rate
|
||
height_downsampled_post = height//downsample_rate
|
||
|
||
if downsample_method == 'Lanczos':
|
||
aliasing = Image.LANCZOS
|
||
else:
|
||
aliasing = Image.NEAREST
|
||
|
||
if downsample_rate != 1:
|
||
print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')
|
||
a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
|
||
elif post_downsample == 'Original Size':
|
||
print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')
|
||
a = a.resize((width_og, height_og), aliasing)
|
||
|
||
#display.display(a)
|
||
#a.save(f'{output_directory}/{img}')
|
||
del model
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
'''
|
||
if import_method != 'Google Drive' and zip_if_not_drive is True:
|
||
print('Zipping files')
|
||
current_time = datetime.now().strftime('%y%m%d-%H%M%S_%f')
|
||
output_zip_name = 'output'+str(current_time)+'.zip'
|
||
#!zip -r {output_zip_name} {output_directory}
|
||
print(f'Zipped outputs in {output_zip_name}')
|
||
'''
|
||
print(f'Processing finished!')
|
||
return a
|
||
|
||
|
||
@torch.no_grad()
|
||
def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
v = torch.randint_like(x, 2) * 2 - 1
|
||
fevals = 0
|
||
def ode_fn(sigma, x):
|
||
nonlocal fevals
|
||
with torch.enable_grad():
|
||
x = x[0].detach().requires_grad_()
|
||
denoised = model(x, sigma * s_in, **extra_args)
|
||
d = to_d(x, sigma, denoised)
|
||
fevals += 1
|
||
grad = torch.autograd.grad((d * v).sum(), x)[0]
|
||
d_ll = (v * grad).flatten(1).sum(1)
|
||
return d.detach(), d_ll
|
||
x_min = x, x.new_zeros([x.shape[0]])
|
||
t = x.new_tensor([sigma_min, sigma_max])
|
||
sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
|
||
latent, delta_ll = sol[0][-1], sol[1][-1]
|
||
ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
|
||
return ll_prior + delta_ll, {'fevals': fevals}
|
||
|
||
|
||
def create_random_tensors(shape, seeds):
|
||
xs = []
|
||
for seed in seeds:
|
||
torch.manual_seed(seed)
|
||
|
||
# randn results depend on device; gpu and cpu get different results for same seed;
|
||
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
|
||
# but the original script had it like this so i do not dare change it for now because
|
||
# it will break everyone's seeds.
|
||
xs.append(torch.randn(shape, device=st.session_state['defaults'].general.gpu))
|
||
x = torch.stack(xs)
|
||
return x
|
||
|
||
def torch_gc():
|
||
torch.cuda.empty_cache()
|
||
torch.cuda.ipc_collect()
|
||
|
||
@retry(tries=5)
|
||
#@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
|
||
def load_GFPGAN(model_name='GFPGANv1.3'):
|
||
#model_name = 'GFPGANv1.3'
|
||
|
||
model_path = os.path.join(st.session_state['defaults'].general.GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
||
|
||
if not os.path.isfile(model_path):
|
||
model_path = os.path.join(st.session_state['defaults'].general.GFPGAN_dir, model_name + '.pth')
|
||
|
||
if not os.path.isfile(model_path):
|
||
raise Exception("GFPGAN model not found at path "+model_path)
|
||
|
||
sys.path.append(os.path.abspath(st.session_state['defaults'].general.GFPGAN_dir))
|
||
from gfpgan import GFPGANer
|
||
|
||
with server_state_lock['GFPGAN']:
|
||
if st.session_state['defaults'].general.gfpgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
|
||
server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean',
|
||
channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
|
||
|
||
elif st.session_state['defaults'].general.extra_models_gpu:
|
||
server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None,
|
||
device=torch.device(f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"))
|
||
else:
|
||
server_state['GFPGAN'] = GFPGANer(model_path=model_path, upscale=1, arch='clean',
|
||
channel_multiplier=2, bg_upsampler=None,
|
||
device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
|
||
|
||
# Add the model_name to model loaded so we can later
|
||
# check if its the same when we change it on the UI.
|
||
server_state['GFPGAN'].name = model_name
|
||
|
||
return server_state['GFPGAN']
|
||
|
||
@retry(tries=5)
|
||
def load_RealESRGAN(model_name: str):
|
||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||
RealESRGAN_models = {
|
||
'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
|
||
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
||
}
|
||
|
||
model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, 'experiments/pretrained_models', model_name + '.pth')
|
||
|
||
if not os.path.isfile(model_path):
|
||
model_path = os.path.join(st.session_state['defaults'].general.RealESRGAN_dir, model_name + '.pth')
|
||
|
||
if not os.path.exists(model_path):
|
||
raise Exception(model_name+".pth not found at path "+model_path)
|
||
|
||
sys.path.append(os.path.abspath(st.session_state['defaults'].general.RealESRGAN_dir))
|
||
from realesrgan import RealESRGANer
|
||
|
||
with server_state_lock['RealESRGAN']:
|
||
if st.session_state['defaults'].general.esrgan_cpu or st.session_state['defaults'].general.extra_models_cpu:
|
||
server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name],
|
||
pre_pad=0, half=False) # cpu does not support half
|
||
|
||
server_state['RealESRGAN'].device = torch.device('cpu')
|
||
server_state['RealESRGAN'].model.to('cpu')
|
||
|
||
elif st.session_state['defaults'].general.extra_models_gpu:
|
||
server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name],
|
||
pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"))
|
||
else:
|
||
server_state['RealESRGAN'] = RealESRGANer(scale=2, model_path=model_path, model=RealESRGAN_models[model_name],
|
||
pre_pad=0, half=not st.session_state['defaults'].general.no_half, device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"))
|
||
|
||
# Add the model_name to model loaded so we can later
|
||
# check if its the same when we change it on the UI.
|
||
server_state['RealESRGAN'].model.name = model_name
|
||
|
||
return server_state['RealESRGAN']
|
||
|
||
#
|
||
@retry(tries=5)
|
||
def load_LDSR(model_name="model", config="project", checking=False):
|
||
#model_name = 'model'
|
||
#yaml_name = 'project'
|
||
|
||
model_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, model_name + '.ckpt')
|
||
yaml_path = os.path.join(st.session_state['defaults'].general.LDSR_dir, config + '.yaml')
|
||
|
||
if not os.path.isfile(model_path):
|
||
raise Exception("LDSR model not found at path " + model_path)
|
||
if not os.path.isfile(yaml_path):
|
||
raise Exception("LDSR model not found at path " + yaml_path)
|
||
if checking == True:
|
||
return True
|
||
|
||
#sys.path.append(os.path.abspath(st.session_state['defaults'].general.LDSR_dir))
|
||
#from LDSR import LDSR
|
||
server_state['LDSR'] = LDSR(model_path, yaml_path)
|
||
|
||
server_state['LDSR'].name = model_name
|
||
|
||
return server_state['LDSR']
|
||
|
||
#
|
||
|
||
@retry(tries=5)
|
||
#def try_loading_LDSR(model_name: str,checking=False):
|
||
##LDSR = None
|
||
##global LDSR
|
||
#if os.path.exists(st.session_state['defaults'].general.LDSR_dir):
|
||
#try:
|
||
#server_state["LDSR"] = load_LDSR(checking=True) # TODO: Should try to load both models before giving up
|
||
#if checking == True:
|
||
#print("Found LDSR")
|
||
#return True
|
||
#print("Latent Diffusion Super Sampling (LDSR) model loaded")
|
||
#except Exception:
|
||
#import traceback
|
||
#print("Error loading LDSR:", file=sys.stderr)
|
||
#print(traceback.format_exc(), file=sys.stderr)
|
||
#else:
|
||
#print("LDSR not found at path, please make sure you have cloned the LDSR repo to ./src/latent-diffusion/")
|
||
|
||
#try_loading_LDSR('model',checking=True)
|
||
|
||
|
||
#@retry(tries=5)
|
||
def load_sd_model(model_name: str):
|
||
"""Loads Stable Diffusion model by name"""
|
||
ckpt_path = st.session_state.defaults.general.default_model_path
|
||
|
||
if model_name != st.session_state.defaults.general.default_model:
|
||
ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")
|
||
|
||
if st.session_state.defaults.general.optimized:
|
||
config = OmegaConf.load(st.session_state.defaults.general.optimized_config)
|
||
|
||
sd = load_sd_from_config(ckpt_path)
|
||
li, lo = [], []
|
||
for key, v_ in sd.items():
|
||
sp = key.split('.')
|
||
if (sp[0]) == 'model':
|
||
if 'input_blocks' in sp:
|
||
li.append(key)
|
||
elif 'middle_block' in sp:
|
||
li.append(key)
|
||
elif 'time_embed' in sp:
|
||
li.append(key)
|
||
else:
|
||
lo.append(key)
|
||
for key in li:
|
||
sd['model1.' + key[6:]] = sd.pop(key)
|
||
for key in lo:
|
||
sd['model2.' + key[6:]] = sd.pop(key)
|
||
|
||
device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
|
||
if torch.cuda.is_available() else torch.device("cpu")
|
||
|
||
model = instantiate_from_config(config.modelUNet)
|
||
_, _ = model.load_state_dict(sd, strict=False)
|
||
model.cuda()
|
||
model.eval()
|
||
model.turbo = st.session_state.defaults.general.optimized_turbo
|
||
|
||
modelCS = instantiate_from_config(config.modelCondStage)
|
||
_, _ = modelCS.load_state_dict(sd, strict=False)
|
||
modelCS.cond_stage_model.device = device
|
||
modelCS.eval()
|
||
|
||
modelFS = instantiate_from_config(config.modelFirstStage)
|
||
_, _ = modelFS.load_state_dict(sd, strict=False)
|
||
modelFS.eval()
|
||
|
||
del sd
|
||
|
||
if not st.session_state.defaults.general.no_half:
|
||
model = model.half().to(device)
|
||
modelCS = modelCS.half().to(device)
|
||
modelFS = modelFS.half().to(device)
|
||
|
||
return config, device, model, modelCS, modelFS
|
||
else:
|
||
config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
|
||
model = load_model_from_config(config, ckpt_path)
|
||
|
||
device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
|
||
if torch.cuda.is_available() else torch.device("cpu")
|
||
model = (model if st.session_state.defaults.general.no_half
|
||
else model.half()).to(device)
|
||
|
||
return config, device, model, None, None
|
||
|
||
|
||
def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
|
||
#codedealer: No usages
|
||
#get global variables
|
||
global_vars = globals()
|
||
#check if m is in globals
|
||
if unload:
|
||
for m in models:
|
||
if m in global_vars:
|
||
#if it is, delete it
|
||
del global_vars[m]
|
||
if st.session_state['defaults'].general.optimized:
|
||
if m == 'model':
|
||
del global_vars[m+'FS']
|
||
del global_vars[m+'CS']
|
||
if m == 'model':
|
||
m = 'Stable Diffusion'
|
||
print('Unloaded ' + m)
|
||
if load:
|
||
for m in models:
|
||
if m not in global_vars or m in global_vars and type(global_vars[m]) == bool:
|
||
#if it isn't, load it
|
||
if m == 'GFPGAN':
|
||
global_vars[m] = load_GFPGAN()
|
||
elif m == 'model':
|
||
sdLoader = load_sd_from_config()
|
||
global_vars[m] = sdLoader[0]
|
||
if st.session_state['defaults'].general.optimized:
|
||
global_vars[m+'CS'] = sdLoader[1]
|
||
global_vars[m+'FS'] = sdLoader[2]
|
||
elif m == 'RealESRGAN':
|
||
global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
|
||
elif m == 'LDSR':
|
||
global_vars[m] = load_LDSR()
|
||
if m =='model':
|
||
m='Stable Diffusion'
|
||
print('Loaded ' + m)
|
||
torch_gc()
|
||
|
||
|
||
#
|
||
@retry(tries=5)
|
||
def generation_callback(img, i=0):
|
||
if "update_preview_frequency" not in st.session_state:
|
||
raise StopException
|
||
|
||
try:
|
||
if i == 0:
|
||
if img['i']: i = img['i']
|
||
except TypeError:
|
||
pass
|
||
|
||
if st.session_state.update_preview and\
|
||
int(st.session_state.update_preview_frequency) > 0 and\
|
||
i % int(st.session_state.update_preview_frequency) == 0 and\
|
||
i > 0:
|
||
#print (img)
|
||
#print (type(img))
|
||
# The following lines will convert the tensor we got on img to an actual image we can render on the UI.
|
||
# It can probably be done in a better way for someone who knows what they're doing. I don't.
|
||
#print (img,isinstance(img, torch.Tensor))
|
||
if isinstance(img, torch.Tensor):
|
||
x_samples_ddim = (server_state["model"].to('cuda') if not st.session_state['defaults'].general.optimized else server_state["modelFS"].to('cuda')
|
||
).decode_first_stage(img).to('cuda')
|
||
else:
|
||
# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
|
||
# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
|
||
x_samples_ddim = (server_state["model"].to('cuda') if not st.session_state['defaults'].general.optimized else server_state["modelFS"].to('cuda')
|
||
).decode_first_stage(img["denoised"]).to('cuda')
|
||
|
||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||
|
||
if x_samples_ddim.ndimension() == 4:
|
||
pil_images = [transforms.ToPILImage()(x.squeeze_(0)) for x in x_samples_ddim]
|
||
pil_image = image_grid(pil_images, 1)
|
||
else:
|
||
pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))
|
||
|
||
|
||
# update image on the UI so we can see the progress
|
||
st.session_state["preview_image"].image(pil_image)
|
||
|
||
# Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
|
||
# Dont worry, it doesnt affect the performance.
|
||
if st.session_state["generation_mode"] == "txt2img":
|
||
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
|
||
st.session_state["progress_bar_text"].text(
|
||
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%")
|
||
else:
|
||
if st.session_state["generation_mode"] == "img2img":
|
||
round_sampling_steps = round(st.session_state.sampling_steps * st.session_state["denoising_strength"])
|
||
percent = int(100 * float(i+1 if i+1 < round_sampling_steps else round_sampling_steps)/float(round_sampling_steps))
|
||
st.session_state["progress_bar_text"].text(
|
||
f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%""")
|
||
else:
|
||
if st.session_state["generation_mode"] == "txt2vid":
|
||
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
|
||
st.session_state["progress_bar_text"].text(
|
||
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
|
||
f"{percent if percent < 100 else 100}%")
|
||
|
||
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
|
||
|
||
|
||
prompt_parser = re.compile("""
|
||
(?P<prompt> # capture group for 'prompt'
|
||
[^:]+ # match one or more non ':' characters
|
||
) # end 'prompt'
|
||
(?: # non-capture group
|
||
:+ # match one or more ':' characters
|
||
(?P<weight> # capture group for 'weight'
|
||
-?\\d+(?:\\.\\d+)? # match positive or negative decimal number
|
||
)? # end weight capture group, make optional
|
||
\\s* # strip spaces after weight
|
||
| # OR
|
||
$ # else, if no ':' then match end of line
|
||
) # end non-capture group
|
||
""", re.VERBOSE)
|
||
|
||
def split_weighted_subprompts(input_string, normalize=True):
|
||
# grabs all text up to the first occurrence of ':' as sub-prompt
|
||
# takes the value following ':' as weight
|
||
# if ':' has no value defined, defaults to 1.0
|
||
# repeats until no text remaining
|
||
parsed_prompts = [(match.group("prompt"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, input_string)]
|
||
if not normalize:
|
||
return parsed_prompts
|
||
# this probably still doesn't handle negative weights very well
|
||
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
|
||
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
|
||
|
||
def slerp(device, t, v0:torch.Tensor, v1:torch.Tensor, DOT_THRESHOLD=0.9995):
|
||
v0 = v0.detach().cpu().numpy()
|
||
v1 = v1.detach().cpu().numpy()
|
||
|
||
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
|
||
if np.abs(dot) > DOT_THRESHOLD:
|
||
v2 = (1 - t) * v0 + t * v1
|
||
else:
|
||
theta_0 = np.arccos(dot)
|
||
sin_theta_0 = np.sin(theta_0)
|
||
theta_t = theta_0 * t
|
||
sin_theta_t = np.sin(theta_t)
|
||
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
|
||
s1 = sin_theta_t / sin_theta_0
|
||
v2 = s0 * v0 + s1 * v1
|
||
|
||
v2 = torch.from_numpy(v2).to(device)
|
||
|
||
return v2
|
||
|
||
#
|
||
@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
|
||
def optimize_update_preview_frequency(current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list):
|
||
"""Find the optimal update_preview_frequency value maximizing
|
||
performance while minimizing the time between updates."""
|
||
from statistics import mean
|
||
|
||
previous_chunk_avg_speed = mean(previous_chunk_speed_list)
|
||
|
||
previous_chunk_speed_list.append(current_chunk_speed)
|
||
current_chunk_avg_speed = mean(previous_chunk_speed_list)
|
||
|
||
if current_chunk_avg_speed >= previous_chunk_avg_speed:
|
||
#print(f"{current_chunk_speed} >= {previous_chunk_speed}")
|
||
update_preview_frequency_list.append(update_preview_frequency + 1)
|
||
else:
|
||
#print(f"{current_chunk_speed} <= {previous_chunk_speed}")
|
||
update_preview_frequency_list.append(update_preview_frequency - 1)
|
||
|
||
update_preview_frequency = round(mean(update_preview_frequency_list))
|
||
|
||
return current_chunk_speed, previous_chunk_speed_list, update_preview_frequency, update_preview_frequency_list
|
||
|
||
|
||
def get_font(fontsize):
|
||
fonts = ["arial.ttf", "DejaVuSans.ttf"]
|
||
for font_name in fonts:
|
||
try:
|
||
return ImageFont.truetype(font_name, fontsize)
|
||
except OSError:
|
||
pass
|
||
|
||
# ImageFont.load_default() is practically unusable as it only supports
|
||
# latin1, so raise an exception instead if no usable font was found
|
||
raise Exception(f"No usable font found (tried {', '.join(fonts)})")
|
||
|
||
def load_embeddings(fp):
|
||
if fp is not None and hasattr(server_state["model"], "embedding_manager"):
|
||
server_state["model"].embedding_manager.load(fp['name'])
|
||
|
||
def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):
|
||
loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
|
||
|
||
# separate token and the embeds
|
||
if learned_embeds_path.endswith('.pt'):
|
||
print(loaded_learned_embeds['string_to_token'])
|
||
trained_token = list(loaded_learned_embeds['string_to_token'].keys())[0]
|
||
embeds = list(loaded_learned_embeds['string_to_param'].values())[0]
|
||
|
||
elif learned_embeds_path.endswith('.bin'):
|
||
trained_token = list(loaded_learned_embeds.keys())[0]
|
||
embeds = loaded_learned_embeds[trained_token]
|
||
|
||
embeds = loaded_learned_embeds[trained_token]
|
||
# cast to dtype of text_encoder
|
||
dtype = text_encoder.get_input_embeddings().weight.dtype
|
||
embeds.to(dtype)
|
||
|
||
# add the token in tokenizer
|
||
token = token if token is not None else trained_token
|
||
num_added_tokens = tokenizer.add_tokens(token)
|
||
|
||
# resize the token embeddings
|
||
text_encoder.resize_token_embeddings(len(tokenizer))
|
||
|
||
# get the id for the token and assign the embeds
|
||
token_id = tokenizer.convert_tokens_to_ids(token)
|
||
text_encoder.get_input_embeddings().weight.data[token_id] = embeds
|
||
return token
|
||
|
||
def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
|
||
#print (len(imgs))
|
||
if force_n_rows is not None:
|
||
rows = force_n_rows
|
||
elif st.session_state['defaults'].general.n_rows > 0:
|
||
rows = st.session_state['defaults'].general.n_rows
|
||
elif st.session_state['defaults'].general.n_rows == 0:
|
||
rows = batch_size
|
||
else:
|
||
rows = math.sqrt(len(imgs))
|
||
rows = round(rows)
|
||
|
||
cols = math.ceil(len(imgs) / rows)
|
||
|
||
w, h = imgs[0].size
|
||
grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
|
||
|
||
fnt = get_font(30)
|
||
|
||
for i, img in enumerate(imgs):
|
||
grid.paste(img, box=(i % cols * w, i // cols * h))
|
||
if captions and i<len(captions):
|
||
d = ImageDraw.Draw( grid )
|
||
size = d.textbbox( (0,0), captions[i], font=fnt, stroke_width=2, align="center" )
|
||
d.multiline_text((i % cols * w + w/2, i // cols * h + h - size[3]), captions[i], font=fnt, fill=(255,255,255), stroke_width=2, stroke_fill=(0,0,0), anchor="mm", align="center")
|
||
|
||
return grid
|
||
|
||
def seed_to_int(s):
|
||
if type(s) is int:
|
||
return s
|
||
if s is None or s == '':
|
||
return random.randint(0, 2**32 - 1)
|
||
|
||
if type(s) is list:
|
||
seed_list = []
|
||
for seed in s:
|
||
if seed is None or seed == '':
|
||
seed_list.append(random.randint(0, 2**32 - 1))
|
||
else:
|
||
seed_list = s
|
||
|
||
return seed_list
|
||
|
||
n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
|
||
while n >= 2**32:
|
||
n = n >> 32
|
||
return n
|
||
|
||
#
|
||
def draw_prompt_matrix(im, width, height, all_prompts):
|
||
def wrap(text, d, font, line_length):
|
||
lines = ['']
|
||
for word in text.split():
|
||
line = f'{lines[-1]} {word}'.strip()
|
||
if d.textlength(line, font=font) <= line_length:
|
||
lines[-1] = line
|
||
else:
|
||
lines.append(word)
|
||
return '\n'.join(lines)
|
||
|
||
def draw_texts(pos, x, y, texts, sizes):
|
||
for i, (text, size) in enumerate(zip(texts, sizes)):
|
||
active = pos & (1 << i) != 0
|
||
|
||
if not active:
|
||
text = '\u0336'.join(text) + '\u0336'
|
||
|
||
d.multiline_text((x, y + size[1] / 2), text, font=fnt, fill=color_active if active else color_inactive, anchor="mm", align="center")
|
||
|
||
y += size[1] + line_spacing
|
||
|
||
fontsize = (width + height) // 25
|
||
line_spacing = fontsize // 2
|
||
fnt = get_font(fontsize)
|
||
color_active = (0, 0, 0)
|
||
color_inactive = (153, 153, 153)
|
||
|
||
pad_top = height // 4
|
||
pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0
|
||
|
||
cols = im.width // width
|
||
rows = im.height // height
|
||
|
||
prompts = all_prompts[1:]
|
||
|
||
result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
|
||
result.paste(im, (pad_left, pad_top))
|
||
|
||
d = ImageDraw.Draw(result)
|
||
|
||
boundary = math.ceil(len(prompts) / 2)
|
||
prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
|
||
prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]
|
||
|
||
sizes_hor = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]]
|
||
sizes_ver = [(x[2] - x[0], x[3] - x[1]) for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]]
|
||
hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
|
||
ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing
|
||
|
||
for col in range(cols):
|
||
x = pad_left + width * col + width / 2
|
||
y = pad_top / 2 - hor_text_height / 2
|
||
|
||
draw_texts(col, x, y, prompts_horiz, sizes_hor)
|
||
|
||
for row in range(rows):
|
||
x = pad_left / 2
|
||
y = pad_top + height * row + height / 2 - ver_text_height / 2
|
||
|
||
draw_texts(row, x, y, prompts_vert, sizes_ver)
|
||
|
||
return result
|
||
|
||
#
|
||
def enable_minimal_memory_usage(model):
|
||
"""Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs"""
|
||
model.unet.to(torch.float16).to(torch.device("cuda"))
|
||
model.enable_attention_slicing(1)
|
||
|
||
torch.cuda.empty_cache()
|
||
torch_gc()
|
||
|
||
def check_prompt_length(prompt, comments):
|
||
"""this function tests if prompt is too long, and if so, adds a message to comments"""
|
||
|
||
tokenizer = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).cond_stage_model.tokenizer
|
||
max_length = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).cond_stage_model.max_length
|
||
|
||
info = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
|
||
return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
|
||
ovf = info['overflowing_tokens'][0]
|
||
overflowing_count = ovf.shape[0]
|
||
if overflowing_count == 0:
|
||
return
|
||
|
||
vocab = {v: k for k, v in tokenizer.get_vocab().items()}
|
||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||
overflowing_text = tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||
|
||
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||
|
||
#
|
||
def custom_models_available():
|
||
with server_state_lock["custom_models"]:
|
||
#
|
||
# Allow for custom models to be used instead of the default one,
|
||
# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
|
||
server_state["custom_models"]:sorted = []
|
||
|
||
for root, dirs, files in os.walk(os.path.join("models", "custom")):
|
||
for file in files:
|
||
if os.path.splitext(file)[1] == '.ckpt':
|
||
server_state["custom_models"].append(os.path.splitext(file)[0])
|
||
|
||
with server_state_lock["CustomModel_available"]:
|
||
if len(server_state["custom_models"]) > 0:
|
||
server_state["CustomModel_available"] = True
|
||
server_state["custom_models"].append("Stable Diffusion v1.4")
|
||
else:
|
||
server_state["CustomModel_available"] = False
|
||
|
||
#
|
||
def GFPGAN_available():
|
||
#with server_state_lock["GFPGAN_models"]:
|
||
#
|
||
# Allow for custom models to be used instead of the default one,
|
||
# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
|
||
st.session_state["GFPGAN_models"]:sorted = []
|
||
|
||
for root, dirs, files in os.walk(st.session_state['defaults'].general.GFPGAN_dir):
|
||
for file in files:
|
||
if os.path.splitext(file)[1] == '.pth':
|
||
st.session_state["GFPGAN_models"].append(os.path.splitext(file)[0])
|
||
|
||
#print (len(st.session_state["GFPGAN_models"]))
|
||
#with server_state_lock["GFPGAN_available"]:
|
||
if len(st.session_state["GFPGAN_models"]) > 0:
|
||
st.session_state["GFPGAN_available"] = True
|
||
else:
|
||
st.session_state["GFPGAN_available"] = False
|
||
|
||
#
|
||
def RealESRGAN_available():
|
||
#with server_state_lock["RealESRGAN_models"]:
|
||
#
|
||
# Allow for custom models to be used instead of the default one,
|
||
# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
|
||
st.session_state["RealESRGAN_models"]:sorted = []
|
||
|
||
for root, dirs, files in os.walk(st.session_state['defaults'].general.RealESRGAN_dir):
|
||
for file in files:
|
||
if os.path.splitext(file)[1] == '.pth':
|
||
st.session_state["RealESRGAN_models"].append(os.path.splitext(file)[0])
|
||
|
||
#with server_state_lock["RealESRGAN_available"]:
|
||
if len(st.session_state["RealESRGAN_models"]) > 0:
|
||
st.session_state["RealESRGAN_available"] = True
|
||
else:
|
||
st.session_state["RealESRGAN_available"] = False
|
||
#
|
||
def LDSR_available():
|
||
#with server_state_lock["RealESRGAN_models"]:
|
||
#
|
||
# Allow for custom models to be used instead of the default one,
|
||
# an example would be Waifu-Diffusion or any other fine tune of stable diffusion
|
||
st.session_state["LDSR_models"]:sorted = []
|
||
|
||
for root, dirs, files in os.walk(st.session_state['defaults'].general.LDSR_dir):
|
||
for file in files:
|
||
if os.path.splitext(file)[1] == '.ckpt':
|
||
st.session_state["LDSR_models"].append(os.path.splitext(file)[0])
|
||
|
||
#print (st.session_state['defaults'].general.LDSR_dir)
|
||
#print (st.session_state["LDSR_models"])
|
||
#with server_state_lock["LDSR_available"]:
|
||
if len(st.session_state["LDSR_models"]) > 0:
|
||
st.session_state["LDSR_available"] = True
|
||
else:
|
||
st.session_state["LDSR_available"] = False
|
||
|
||
|
||
def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images, model_name):
|
||
|
||
filename_i = os.path.join(sample_path_i, filename)
|
||
|
||
if st.session_state['defaults'].general.save_metadata or write_info_files:
|
||
# toggles differ for txt2img vs. img2img:
|
||
offset = 0 if init_img is None else 2
|
||
toggles = []
|
||
if prompt_matrix:
|
||
toggles.append(0)
|
||
if normalize_prompt_weights:
|
||
toggles.append(1)
|
||
if init_img is not None:
|
||
if uses_loopback:
|
||
toggles.append(2)
|
||
if uses_random_seed_loopback:
|
||
toggles.append(3)
|
||
if save_individual_images:
|
||
toggles.append(2 + offset)
|
||
if save_grid:
|
||
toggles.append(3 + offset)
|
||
if sort_samples:
|
||
toggles.append(4 + offset)
|
||
if write_info_files:
|
||
toggles.append(5 + offset)
|
||
if use_GFPGAN:
|
||
toggles.append(6 + offset)
|
||
metadata = \
|
||
dict(
|
||
target="txt2img" if init_img is None else "img2img",
|
||
prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
|
||
ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
|
||
seed=seeds[i], width=width, height=height, normalize_prompt_weights=normalize_prompt_weights, model_name=server_state["loaded_model"])
|
||
# Not yet any use for these, but they bloat up the files:
|
||
# info_dict["init_img"] = init_img
|
||
# info_dict["init_mask"] = init_mask
|
||
if init_img is not None:
|
||
metadata["denoising_strength"] = str(denoising_strength)
|
||
metadata["resize_mode"] = resize_mode
|
||
|
||
if write_info_files:
|
||
with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
|
||
yaml.dump(metadata, f, allow_unicode=True, width=10000)
|
||
|
||
if st.session_state['defaults'].general.save_metadata:
|
||
# metadata = {
|
||
# "SD:prompt": prompts[i],
|
||
# "SD:seed": str(seeds[i]),
|
||
# "SD:width": str(width),
|
||
# "SD:height": str(height),
|
||
# "SD:steps": str(steps),
|
||
# "SD:cfg_scale": str(cfg_scale),
|
||
# "SD:normalize_prompt_weights": str(normalize_prompt_weights),
|
||
# }
|
||
metadata = {"SD:" + k:v for (k,v) in metadata.items()}
|
||
|
||
if save_ext == "png":
|
||
mdata = PngInfo()
|
||
for key in metadata:
|
||
mdata.add_text(key, str(metadata[key]))
|
||
image.save(f"{filename_i}.png", pnginfo=mdata)
|
||
else:
|
||
if jpg_sample:
|
||
image.save(f"{filename_i}.jpg", quality=save_quality,
|
||
optimize=True)
|
||
elif save_ext == "webp":
|
||
image.save(f"{filename_i}.{save_ext}", f"webp", quality=save_quality,
|
||
lossless=save_lossless)
|
||
else:
|
||
# not sure what file format this is
|
||
image.save(f"{filename_i}.{save_ext}", f"{save_ext}")
|
||
try:
|
||
exif_dict = piexif.load(f"{filename_i}.{save_ext}")
|
||
except:
|
||
exif_dict = { "Exif": dict() }
|
||
exif_dict["Exif"][piexif.ExifIFD.UserComment] = piexif.helper.UserComment.dump(
|
||
json.dumps(metadata), encoding="unicode")
|
||
piexif.insert(piexif.dump(exif_dict), f"{filename_i}.{save_ext}")
|
||
|
||
|
||
def get_next_sequence_number(path, prefix=''):
|
||
"""
|
||
Determines and returns the next sequence number to use when saving an
|
||
image in the specified directory.
|
||
|
||
If a prefix is given, only consider files whose names start with that
|
||
prefix, and strip the prefix from filenames before extracting their
|
||
sequence number.
|
||
|
||
The sequence starts at 0.
|
||
"""
|
||
result = -1
|
||
for p in Path(path).iterdir():
|
||
if p.name.endswith(('.png', '.jpg')) and p.name.startswith(prefix):
|
||
tmp = p.name[len(prefix):]
|
||
try:
|
||
result = max(int(tmp.split('-')[0]), result)
|
||
except ValueError:
|
||
pass
|
||
return result + 1
|
||
|
||
|
||
def oxlamon_matrix(prompt, seed, n_iter, batch_size):
|
||
pattern = re.compile(r'(,\s){2,}')
|
||
|
||
class PromptItem:
|
||
def __init__(self, text, parts, item):
|
||
self.text = text
|
||
self.parts = parts
|
||
if item:
|
||
self.parts.append( item )
|
||
|
||
def clean(txt):
|
||
return re.sub(pattern, ', ', txt)
|
||
|
||
def getrowcount( txt ):
|
||
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
|
||
if data:
|
||
return len(data.group(1).split("|"))
|
||
break
|
||
return None
|
||
|
||
def repliter( txt ):
|
||
for data in re.finditer( ".*?\\((.*?)\\).*", txt ):
|
||
if data:
|
||
r = data.span(1)
|
||
for item in data.group(1).split("|"):
|
||
yield (clean(txt[:r[0]-1] + item.strip() + txt[r[1]+1:]), item.strip())
|
||
break
|
||
|
||
def iterlist( items ):
|
||
outitems = []
|
||
for item in items:
|
||
for newitem, newpart in repliter(item.text):
|
||
outitems.append( PromptItem(newitem, item.parts.copy(), newpart) )
|
||
|
||
return outitems
|
||
|
||
def getmatrix( prompt ):
|
||
dataitems = [ PromptItem( prompt[1:].strip(), [], None ) ]
|
||
while True:
|
||
newdataitems = iterlist( dataitems )
|
||
if len( newdataitems ) == 0:
|
||
return dataitems
|
||
dataitems = newdataitems
|
||
|
||
def classToArrays( items, seed, n_iter ):
|
||
texts = []
|
||
parts = []
|
||
seeds = []
|
||
|
||
for item in items:
|
||
itemseed = seed
|
||
for i in range(n_iter):
|
||
texts.append( item.text )
|
||
parts.append( f"Seed: {itemseed}\n" + "\n".join(item.parts) )
|
||
seeds.append( itemseed )
|
||
itemseed += 1
|
||
|
||
return seeds, texts, parts
|
||
|
||
all_seeds, all_prompts, prompt_matrix_parts = classToArrays(getmatrix( prompt ), seed, n_iter)
|
||
n_iter = math.ceil(len(all_prompts) / batch_size)
|
||
|
||
needrows = getrowcount(prompt)
|
||
if needrows:
|
||
xrows = math.sqrt(len(all_prompts))
|
||
xrows = round(xrows)
|
||
# if columns is to much
|
||
cols = math.ceil(len(all_prompts) / xrows)
|
||
if cols > needrows*4:
|
||
needrows *= 2
|
||
|
||
return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows
|
||
|
||
#
|
||
def process_images(
|
||
outpath, func_init, func_sample, prompt, seed, sampler_name, save_grid, batch_size,
|
||
n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3',
|
||
use_RealESRGAN: bool = False, realesrgan_model_name:str = 'RealESRGAN_x4plus',
|
||
use_LDSR:bool = False, LDSR_model_name:str = 'model', ddim_eta=0.0, normalize_prompt_weights=True, init_img=None, init_mask=None,
|
||
mask_blur_strength=3, mask_restore=False, denoising_strength=0.75, noise_mode=0, find_noise_steps=1, resize_mode=None, uses_loopback=False,
|
||
uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False,
|
||
variant_amount=0.0, variant_seed=None, save_individual_images: bool = True):
|
||
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
|
||
|
||
torch_gc()
|
||
# start time after garbage collection (or before?)
|
||
start_time = time.time()
|
||
|
||
# We will use this date here later for the folder name, need to start_time if not need
|
||
run_start_dt = datetime.datetime.now()
|
||
|
||
mem_mon = MemUsageMonitor('MemMon')
|
||
mem_mon.start()
|
||
|
||
if st.session_state.defaults.general.use_sd_concepts_library:
|
||
|
||
prompt_tokens = re.findall('<([a-zA-Z0-9-]+)>', prompt)
|
||
|
||
if prompt_tokens:
|
||
# compviz
|
||
tokenizer = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).cond_stage_model.tokenizer
|
||
text_encoder = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).cond_stage_model.transformer
|
||
|
||
# diffusers
|
||
#tokenizer = pipe.tokenizer
|
||
#text_encoder = pipe.text_encoder
|
||
|
||
ext = ('pt', 'bin')
|
||
|
||
if len(prompt_tokens) > 1:
|
||
for token_name in prompt_tokens:
|
||
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, token_name)
|
||
if os.path.exists(embedding_path):
|
||
for files in os.listdir(embedding_path):
|
||
if files.endswith(ext):
|
||
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{token_name}>")
|
||
else:
|
||
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, prompt_tokens[0])
|
||
if os.path.exists(embedding_path):
|
||
for files in os.listdir(embedding_path):
|
||
if files.endswith(ext):
|
||
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{prompt_tokens[0]}>")
|
||
|
||
#
|
||
|
||
|
||
os.makedirs(outpath, exist_ok=True)
|
||
|
||
sample_path = os.path.join(outpath, "samples")
|
||
os.makedirs(sample_path, exist_ok=True)
|
||
|
||
if not ("|" in prompt) and prompt.startswith("@"):
|
||
prompt = prompt[1:]
|
||
|
||
negprompt = ''
|
||
if '###' in prompt:
|
||
prompt, negprompt = prompt.split('###', 1)
|
||
prompt = prompt.strip()
|
||
negprompt = negprompt.strip()
|
||
|
||
comments = []
|
||
|
||
prompt_matrix_parts = []
|
||
simple_templating = False
|
||
add_original_image = not (use_RealESRGAN or use_GFPGAN)
|
||
|
||
if prompt_matrix:
|
||
if prompt.startswith("@"):
|
||
simple_templating = True
|
||
add_original_image = not (use_RealESRGAN or use_GFPGAN)
|
||
all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(prompt, seed, n_iter, batch_size)
|
||
else:
|
||
all_prompts = []
|
||
prompt_matrix_parts = prompt.split("|")
|
||
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
|
||
for combination_num in range(combination_count):
|
||
current = prompt_matrix_parts[0]
|
||
|
||
for n, text in enumerate(prompt_matrix_parts[1:]):
|
||
if combination_num & (2 ** n) > 0:
|
||
current += ("" if text.strip().startswith(",") else ", ") + text
|
||
|
||
all_prompts.append(current)
|
||
|
||
n_iter = math.ceil(len(all_prompts) / batch_size)
|
||
all_seeds = len(all_prompts) * [seed]
|
||
|
||
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches.")
|
||
else:
|
||
|
||
if not st.session_state['defaults'].general.no_verify_input:
|
||
try:
|
||
check_prompt_length(prompt, comments)
|
||
except:
|
||
import traceback
|
||
print("Error verifying input:", file=sys.stderr)
|
||
print(traceback.format_exc(), file=sys.stderr)
|
||
|
||
all_prompts = batch_size * n_iter * [prompt]
|
||
all_seeds = [seed + x for x in range(len(all_prompts))]
|
||
|
||
precision_scope = autocast if st.session_state['defaults'].general.precision == "autocast" else nullcontext
|
||
output_images = []
|
||
grid_captions = []
|
||
stats = []
|
||
with torch.no_grad(), precision_scope("cuda"), (server_state["model"].ema_scope() if not st.session_state['defaults'].general.optimized else nullcontext()):
|
||
init_data = func_init()
|
||
tic = time.time()
|
||
|
||
|
||
# if variant_amount > 0.0 create noise from base seed
|
||
base_x = None
|
||
if variant_amount > 0.0:
|
||
target_seed_randomizer = seed_to_int('') # random seed
|
||
torch.manual_seed(seed) # this has to be the single starting seed (not per-iteration)
|
||
base_x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=[seed])
|
||
# we don't want all_seeds to be sequential from starting seed with variants,
|
||
# since that makes the same variants each time,
|
||
# so we add target_seed_randomizer as a random offset
|
||
for si in range(len(all_seeds)):
|
||
all_seeds[si] += target_seed_randomizer
|
||
|
||
for n in range(n_iter):
|
||
print(f"Iteration: {n+1}/{n_iter}")
|
||
prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
|
||
captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
|
||
seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
|
||
|
||
print(prompt)
|
||
|
||
if st.session_state['defaults'].general.optimized:
|
||
server_state["modelCS"].to(st.session_state['defaults'].general.gpu)
|
||
|
||
uc = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).get_learned_conditioning(len(prompts) * [negprompt])
|
||
|
||
if isinstance(prompts, tuple):
|
||
prompts = list(prompts)
|
||
|
||
# split the prompt if it has : for weighting
|
||
# TODO for speed it might help to have this occur when all_prompts filled??
|
||
weighted_subprompts = split_weighted_subprompts(prompts[0], normalize_prompt_weights)
|
||
|
||
# sub-prompt weighting used if more than 1
|
||
if len(weighted_subprompts) > 1:
|
||
c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
|
||
for i in range(0, len(weighted_subprompts)):
|
||
# note if alpha negative, it functions same as torch.sub
|
||
c = torch.add(c, (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]
|
||
).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
|
||
else: # just behave like usual
|
||
c = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelCS"]).get_learned_conditioning(prompts)
|
||
|
||
|
||
shape = [opt_C, height // opt_f, width // opt_f]
|
||
|
||
if st.session_state['defaults'].general.optimized:
|
||
mem = torch.cuda.memory_allocated()/1e6
|
||
server_state["modelCS"].to("cpu")
|
||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||
time.sleep(1)
|
||
|
||
if noise_mode == 1 or noise_mode == 3:
|
||
# TODO params for find_noise_to_image
|
||
x = torch.cat(batch_size * [find_noise_for_image(
|
||
server_state["model"], server_state["device"],
|
||
init_img.convert('RGB'), '', find_noise_steps, 0.0, normalize=True,
|
||
generation_callback=generation_callback,
|
||
)], dim=0)
|
||
else:
|
||
# we manually generate all input noises because each one should have a specific seed
|
||
x = create_random_tensors(shape, seeds=seeds)
|
||
|
||
if variant_amount > 0.0: # we are making variants
|
||
# using variant_seed as sneaky toggle,
|
||
# when not None or '' use the variant_seed
|
||
# otherwise use seeds
|
||
if variant_seed != None and variant_seed != '':
|
||
specified_variant_seed = seed_to_int(variant_seed)
|
||
torch.manual_seed(specified_variant_seed)
|
||
seeds = [specified_variant_seed]
|
||
# finally, slerp base_x noise to target_x noise for creating a variant
|
||
x = slerp(st.session_state['defaults'].general.gpu, max(0.0, min(1.0, variant_amount)), base_x, x)
|
||
|
||
samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
|
||
|
||
if st.session_state['defaults'].general.optimized:
|
||
server_state["modelFS"].to(st.session_state['defaults'].general.gpu)
|
||
|
||
x_samples_ddim = (server_state["model"] if not st.session_state['defaults'].general.optimized else server_state["modelFS"]).decode_first_stage(samples_ddim)
|
||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||
|
||
run_images = []
|
||
for i, x_sample in enumerate(x_samples_ddim):
|
||
sanitized_prompt = slugify(prompts[i])
|
||
|
||
percent = i / len(x_samples_ddim)
|
||
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
|
||
|
||
if sort_samples:
|
||
full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)
|
||
|
||
|
||
sanitized_prompt = sanitized_prompt[:200-len(full_path)]
|
||
sample_path_i = os.path.join(sample_path, sanitized_prompt)
|
||
|
||
#print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
|
||
#print(os.path.join(os.getcwd(), sample_path_i))
|
||
|
||
os.makedirs(sample_path_i, exist_ok=True)
|
||
base_count = get_next_sequence_number(sample_path_i)
|
||
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
|
||
else:
|
||
full_path = os.path.join(os.getcwd(), sample_path)
|
||
sample_path_i = sample_path
|
||
base_count = get_next_sequence_number(sample_path_i)
|
||
filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[:200-len(full_path)] #same as before
|
||
|
||
x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
|
||
x_sample = x_sample.astype(np.uint8)
|
||
image = Image.fromarray(x_sample)
|
||
original_sample = x_sample
|
||
original_filename = filename
|
||
|
||
st.session_state["preview_image"].image(image)
|
||
|
||
if use_GFPGAN and server_state["GFPGAN"] is not None and not use_RealESRGAN and not use_LDSR:
|
||
st.session_state["progress_bar_text"].text("Running GFPGAN on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
|
||
torch_gc()
|
||
cropped_faces, restored_faces, restored_img = server_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||
|
||
gfpgan_sample = restored_img[:,:,::-1]
|
||
gfpgan_image = Image.fromarray(gfpgan_sample)
|
||
|
||
#if st.session_state["GFPGAN_strenght"]:
|
||
#gfpgan_sample = Image.blend(image, gfpgan_image, st.session_state["GFPGAN_strenght"])
|
||
|
||
gfpgan_filename = original_filename + '-gfpgan'
|
||
|
||
save_sample(gfpgan_image, sample_path_i, gfpgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback,
|
||
uses_random_seed_loopback, save_grid, sort_samples, sampler_name, ddim_eta,
|
||
n_iter, batch_size, i, denoising_strength, resize_mode, False, server_state["loaded_model"])
|
||
|
||
output_images.append(gfpgan_image) #287
|
||
run_images.append(gfpgan_image)
|
||
|
||
if simple_templating:
|
||
grid_captions.append( captions[i] + "\ngfpgan" )
|
||
|
||
#
|
||
elif use_RealESRGAN and server_state["RealESRGAN"] is not None and not use_GFPGAN:
|
||
st.session_state["progress_bar_text"].text("Running RealESRGAN on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
#skip_save = True # #287 >_>
|
||
torch_gc()
|
||
|
||
if server_state["RealESRGAN"].model.name != realesrgan_model_name:
|
||
#try_loading_RealESRGAN(realesrgan_model_name)
|
||
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
||
|
||
output, img_mode = server_state["RealESRGAN"].enhance(x_sample[:,:,::-1])
|
||
esrgan_filename = original_filename + '-esrgan4x'
|
||
esrgan_sample = output[:,:,::-1]
|
||
esrgan_image = Image.fromarray(esrgan_sample)
|
||
|
||
#save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
#normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
||
#save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
|
||
|
||
save_sample(esrgan_image, sample_path_i, esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False, server_state["loaded_model"])
|
||
|
||
output_images.append(esrgan_image) #287
|
||
run_images.append(esrgan_image)
|
||
|
||
if simple_templating:
|
||
grid_captions.append( captions[i] + "\nesrgan" )
|
||
|
||
#
|
||
elif use_LDSR and server_state["LDSR"] is not None and not use_GFPGAN:
|
||
print ("Running LDSR on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
st.session_state["progress_bar_text"].text("Running LDSR on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
#skip_save = True # #287 >_>
|
||
torch_gc()
|
||
|
||
if server_state["LDSR"].name != LDSR_model_name:
|
||
#try_loading_RealESRGAN(realesrgan_model_name)
|
||
load_models(use_LDSR=use_LDSR, LDSR_model=LDSR_model_name, use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
||
|
||
result = server_state["LDSR"].superResolution(image, 2, 2, 2)
|
||
ldsr_filename = original_filename + '-ldsr4x'
|
||
#ldsr_sample = result[:,:,::-1]
|
||
#ldsr_image = Image.fromarray(ldsr_sample)
|
||
|
||
#save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
#normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
||
#save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
|
||
|
||
save_sample(result, sample_path_i, ldsr_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False, server_state["loaded_model"])
|
||
|
||
output_images.append(ldsr_image) #287
|
||
run_images.append(ldsr_image)
|
||
|
||
if simple_templating:
|
||
grid_captions.append( captions[i] + "\nldsr" )
|
||
|
||
#
|
||
elif use_LDSR and server_state["LDSR"] is not None and use_GFPGAN:
|
||
print ("Running GFPGAN+LDSR on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
st.session_state["progress_bar_text"].text("Running GFPGAN+LDSR on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
#skip_save = True # #287 >_>
|
||
torch_gc()
|
||
|
||
if server_state["LDSR"].name != LDSR_model_name:
|
||
#try_loading_RealESRGAN(realesrgan_model_name)
|
||
load_models(use_LDSR=use_LDSR, LDSR_model=LDSR_model_name, use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
||
|
||
result = server_state["LDSR"].superResolution(image, 2, 2, 2)
|
||
ldsr_filename = original_filename + '-gfpgan-ldsr2x'
|
||
#ldsr_sample = result[:,:,::-1]
|
||
#ldsr_image = Image.fromarray(result)
|
||
|
||
#save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
#normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
|
||
#save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
|
||
|
||
save_sample(result, sample_path_i, ldsr_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False, server_state["loaded_model"])
|
||
|
||
output_images.append(result) #287
|
||
run_images.append(result)
|
||
|
||
if simple_templating:
|
||
grid_captions.append( captions[i] + "\ngfpgan-ldsr" )
|
||
|
||
elif use_RealESRGAN and server_state["RealESRGAN"] is not None and use_GFPGAN and server_state["GFPGAN"] is not None:
|
||
st.session_state["progress_bar_text"].text("Running GFPGAN+RealESRGAN on image %d of %d..." % (i+1, len(x_samples_ddim)))
|
||
#skip_save = True # #287 >_>
|
||
torch_gc()
|
||
cropped_faces, restored_faces, restored_img = server_state["GFPGAN"].enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||
gfpgan_sample = restored_img[:,:,::-1]
|
||
|
||
if server_state["RealESRGAN"].model.name != realesrgan_model_name:
|
||
#try_loading_RealESRGAN(realesrgan_model_name)
|
||
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
||
|
||
output, img_mode = server_state["RealESRGAN"].enhance(gfpgan_sample[:,:,::-1])
|
||
gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
|
||
gfpgan_esrgan_sample = output[:,:,::-1]
|
||
gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
|
||
|
||
save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, False, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, False, server_state["loaded_model"])
|
||
|
||
output_images.append(gfpgan_esrgan_image) #287
|
||
run_images.append(gfpgan_esrgan_image)
|
||
|
||
if simple_templating:
|
||
grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
|
||
|
||
#
|
||
|
||
else:
|
||
output_images.append(image)
|
||
run_images.append(image)
|
||
|
||
if mask_restore and init_mask:
|
||
#init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
|
||
init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
|
||
init_mask = init_mask.convert('L')
|
||
init_img = init_img.convert('RGB')
|
||
image = image.convert('RGB')
|
||
|
||
if use_RealESRGAN and server_state["RealESRGAN"] is not None:
|
||
if server_state["RealESRGAN"].model.name != realesrgan_model_name:
|
||
#try_loading_RealESRGAN(realesrgan_model_name)
|
||
load_models(use_GFPGAN=use_GFPGAN, use_RealESRGAN=use_RealESRGAN, RealESRGAN_model=realesrgan_model_name)
|
||
|
||
output, img_mode = server_state["RealESRGAN"].enhance(np.array(init_img, dtype=np.uint8))
|
||
init_img = Image.fromarray(output)
|
||
init_img = init_img.convert('RGB')
|
||
|
||
output, img_mode = server_state["RealESRGAN"].enhance(np.array(init_mask, dtype=np.uint8))
|
||
init_mask = Image.fromarray(output)
|
||
init_mask = init_mask.convert('L')
|
||
|
||
image = Image.composite(init_img, image, init_mask)
|
||
|
||
if save_individual_images:
|
||
save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
|
||
normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback,
|
||
save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode, save_individual_images, server_state["loaded_model"])
|
||
|
||
#if add_original_image or not simple_templating:
|
||
#output_images.append(image)
|
||
#if simple_templating:
|
||
#grid_captions.append( captions[i] )
|
||
|
||
if st.session_state['defaults'].general.optimized:
|
||
mem = torch.cuda.memory_allocated()/1e6
|
||
server_state["modelFS"].to("cpu")
|
||
while(torch.cuda.memory_allocated()/1e6 >= mem):
|
||
time.sleep(1)
|
||
|
||
if len(run_images) > 1:
|
||
preview_image = image_grid(run_images, n_iter)
|
||
else:
|
||
preview_image = run_images[0]
|
||
|
||
# Constrain the final preview image to 1440x900 so we're not sending huge amounts of data
|
||
# to the browser
|
||
preview_image = constrain_image(preview_image, 1440, 900)
|
||
st.session_state["progress_bar_text"].text("Finished!")
|
||
st.session_state["preview_image"].image(preview_image)
|
||
|
||
if prompt_matrix or save_grid:
|
||
if prompt_matrix:
|
||
if simple_templating:
|
||
grid = image_grid(output_images, n_iter, force_n_rows=frows, captions=grid_captions)
|
||
else:
|
||
grid = image_grid(output_images, n_iter, force_n_rows=1 << ((len(prompt_matrix_parts)-1)//2))
|
||
try:
|
||
grid = draw_prompt_matrix(grid, width, height, prompt_matrix_parts)
|
||
except:
|
||
import traceback
|
||
print("Error creating prompt_matrix text:", file=sys.stderr)
|
||
print(traceback.format_exc(), file=sys.stderr)
|
||
else:
|
||
grid = image_grid(output_images, batch_size)
|
||
|
||
if grid and (batch_size > 1 or n_iter > 1):
|
||
output_images.insert(0, grid)
|
||
|
||
grid_count = get_next_sequence_number(outpath, 'grid-')
|
||
grid_file = f"grid-{grid_count:05}-{seed}_{slugify(prompts[i].replace(' ', '_')[:200-len(full_path)])}.{grid_ext}"
|
||
grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
|
||
|
||
toc = time.time()
|
||
|
||
mem_max_used, mem_total = mem_mon.read_and_stop()
|
||
time_diff = time.time()-start_time
|
||
|
||
info = f"""
|
||
{prompt}
|
||
Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and server_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and server_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
|
||
stats = f'''
|
||
Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
|
||
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
|
||
|
||
for comment in comments:
|
||
info += "\n\n" + comment
|
||
|
||
#mem_mon.stop()
|
||
#del mem_mon
|
||
torch_gc()
|
||
|
||
return output_images, seed, info, stats
|
||
|
||
|
||
def resize_image(resize_mode, im, width, height):
|
||
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||
if resize_mode == 0:
|
||
res = im.resize((width, height), resample=LANCZOS)
|
||
elif resize_mode == 1:
|
||
ratio = width / height
|
||
src_ratio = im.width / im.height
|
||
|
||
src_w = width if ratio > src_ratio else im.width * height // im.height
|
||
src_h = height if ratio <= src_ratio else im.height * width // im.width
|
||
|
||
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
||
res = Image.new("RGBA", (width, height))
|
||
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
||
else:
|
||
ratio = width / height
|
||
src_ratio = im.width / im.height
|
||
|
||
src_w = width if ratio < src_ratio else im.width * height // im.height
|
||
src_h = height if ratio >= src_ratio else im.height * width // im.width
|
||
|
||
resized = im.resize((src_w, src_h), resample=LANCZOS)
|
||
res = Image.new("RGBA", (width, height))
|
||
res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
|
||
|
||
if ratio < src_ratio:
|
||
fill_height = height // 2 - src_h // 2
|
||
res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
|
||
res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
|
||
elif ratio > src_ratio:
|
||
fill_width = width // 2 - src_w // 2
|
||
res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
|
||
res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
|
||
|
||
return res
|
||
|
||
def constrain_image(img, max_width, max_height):
|
||
ratio = max(img.width / max_width, img.height / max_height)
|
||
if ratio <= 1:
|
||
return img
|
||
resampler = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
|
||
resized = img.resize((int(img.width / ratio), int(img.height / ratio)), resample=resampler)
|
||
return resized
|
||
|
||
def convert_pt_to_bin_and_load(input_file, text_encoder, tokenizer, placeholder_token):
|
||
x = torch.load(input_file, map_location=torch.device('cpu'))
|
||
|
||
params_dict = {
|
||
placeholder_token: torch.tensor(list(x['string_to_param'].items())[0][1])
|
||
}
|
||
torch.save(params_dict, "learned_embeds.bin")
|
||
load_learned_embed_in_clip("learned_embeds.bin", text_encoder, tokenizer, placeholder_token)
|
||
print("loaded", placeholder_token)
|
||
|