mirror of https://github.com/vladmandic/automatic
jump patch
Vladimir Mandic
parent
e6701f5cb6
commit
0e7144186d
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@ -39,10 +39,5 @@ venv
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# unexcluded so folders get created
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!/repositories/.placeholder
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!/extensions/.placeholder
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!/outputs/.placeholder
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!/models/.placeholder
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!/models/embeddings/.placeholder
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!/models/hypernetworks/.placeholder
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!/models/VAE-approx
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!/models/VAE-approx/model.pt
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5
TODO.md
5
TODO.md
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@ -10,6 +10,8 @@ Stuff to be fixed...
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Stuff to be added...
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- Add README headers
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- Add Gradio base themes: <https://gradio.app/theming-guide/#using-the-theme-builder>
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- Create new GitHub hooks/actions for CI/CD
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- Move Restart Server from WebUI to Launch and reload modules
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- Redo Extensions tab: see <https://vladmandic.github.io/sd-extension-manager/pages/extensions.html>
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@ -54,3 +56,6 @@ Tech that can be integrated as part of the core workflow...
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### Pending Code Updates
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- full CUDA tuning section in UI Settings
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- improve compatibility with some 3rd party extensions
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- improve exif/pnginfo metadata parsing
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@ -1 +1 @@
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Subproject commit bb7d428ac7703d6703ebabbc1fcc9233ac958801
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Subproject commit 94e3375e746ed8caab6d09799f380bed1f29ccd6
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@ -1 +1 @@
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Subproject commit 15c1938bd006638363f762c518a0f48247eff724
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Subproject commit cc86ce8887f041e88e67d336044190fd0296fd74
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@ -1 +1 @@
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Subproject commit 937e8f07414d5de9f4a8570844731d643aeff576
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Subproject commit 4d95eb9ea77023d00b43bffcb59baf2c56cc639c
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@ -102,6 +102,7 @@ svg.feather.feather-image, .feather .feather-image { display: none }
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#txt2img_subseed_strength { margin-top: 0; }
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#txt2img_tools, #img2img_tools { margin-top: 54px; scale: 120%; margin-left: 26px; }
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#txtimg_hr_finalres { max-width: 200px; }
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#pnginfo_html2_info { margin-top: -18px; background-color: var(--input-background-fill); padding: var(--input-padding) }
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/* custom elements overrides */
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#steps-animation, #controlnet { border-width: 0; }
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@ -133,7 +134,7 @@ svg.feather.feather-image, .feather .feather-image { display: none }
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--block_title_background_fill: None;
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--block_title_border_color: None;
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--block_title_border_width: None;
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--block-title-text-color: var(--neutral-200);
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--block-title-text-color: white;
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--panel-background-fill: var(--background-fill-secondary);
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--panel-border-color: var(--border-color-primary);
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--panel_border_width: None;
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@ -3,23 +3,28 @@ import os
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import sys
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import shlex
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import setup
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import modules.paths_internal
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from modules import cmd_args
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from modules.paths_internal import script_path
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try:
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from rich import print # pylint: disable=redefined-builtin
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except ImportError:
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pass
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### majority of this file is superflous, but used by some extensions as helpers during extension installation
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commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
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sys.argv += shlex.split(commandline_args)
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setup.parse_args()
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args, _ = cmd_args.parser.parse_known_args()
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script_path = modules.paths_internal.script_path
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extensions_dir = modules.paths_internal.extensions_dir
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git = os.environ.get('GIT', "git")
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index_url = os.environ.get('INDEX_URL', "")
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stored_commit_hash = None
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dir_repos = "repositories"
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python = sys.executable # used by some extensions to run python
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skip_install = False # parsed by some extensions
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def commit_hash():
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@ -10,10 +10,6 @@ parser.add_argument("--ui-config-file", type=str, help=argparse.SUPPRESS, defaul
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parser.add_argument("--config", type=str, default=sd_default_config, help=argparse.SUPPRESS)
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parser.add_argument("--theme", type=str, help=argparse.SUPPRESS, default=None)
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parser.add_argument("--no-half", action='store_true', help="Do not switch the model to 16-bit floats")
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parser.add_argument("--no-half-vae", action='store_true', help="Do not switch the VAE model to 16-bit floats")
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parser.add_argument("--precision", type=str, help="Evaluate at this precision", choices=["full", "autocast"], default="autocast")
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parser.add_argument("--medvram", action='store_true', help="Enable model optimizations for sacrificing a little speed for low memory usage")
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parser.add_argument("--lowvram", action='store_true', help="Enable model optimizations for sacrificing a lot of speed for lowest memory usage")
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parser.add_argument("--lowram", action='store_true', help="Load checkpoint weights to VRAM instead of RAM")
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@ -61,10 +57,15 @@ def compatibility_args(opts, args):
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parser.add_argument("--bsrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.bsrgan_models_path)
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parser.add_argument("--realesrgan-models-path", type=str, help=argparse.SUPPRESS, default=opts.realesrgan_models_path)
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parser.add_argument("--clip-models-path", type=str, help=argparse.SUPPRESS, default=opts.clip_models_path)
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parser.add_argument("--disable-nan-check", default = True, action='store_true', help=argparse.SUPPRESS)
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parser.add_argument("--disable-extension-access", default = False, action='store_true', help=argparse.SUPPRESS)
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parser.add_argument("--opt-channelslast", help=argparse.SUPPRESS, default=opts.opt_channelslast)
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parser.add_argument("--xformers", default = (opts.cross_attention_optimization == "xFormers"), action='store_true', help=argparse.SUPPRESS)
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parser.add_argument("--disable-nan-check", help=argparse.SUPPRESS, default=opts.disable_nan_check)
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parser.add_argument("--no-half", help=argparse.SUPPRESS, default=opts.no_half)
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parser.add_argument("--no-half-vae", help=argparse.SUPPRESS, default=opts.no_half_vae)
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parser.add_argument("--precision", help=argparse.SUPPRESS, default=opts.precision)
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parser.add_argument("--api", help=argparse.SUPPRESS, default=True)
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args = parser.parse_args()
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if vars(parser)['_option_string_actions'].get('--lora-dir', None) is not None:
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args.lora_dir = opts.lora_dir
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@ -1,7 +1,6 @@
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import sys
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import contextlib
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import torch
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from modules import errors
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if sys.platform == "darwin":
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from modules import mac_specific
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@ -60,23 +59,26 @@ def torch_gc():
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torch.cuda.ipc_collect()
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def enable_tf32():
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if torch.cuda.is_available():
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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def enable_cudnn_benchmark():
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def set_cuda_params():
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if not torch.cuda.is_available():
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return
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from modules import shared
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if torch.backends.cudnn.is_available():
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torch.backends.cudnn.benchmark = shared.opts.cudnn_benchmark
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torch.backends.cudnn.benchmark_limit = 0
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torch.backends.cudnn.allow_tf32 = shared.opts.cuda_allow_tf32
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torch.backends.cuda.matmul.allow_tf32 = shared.opts.cuda_allow_tf32
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torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = shared.opts.cuda_allow_tf16_reduced
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torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = shared.opts.cuda_allow_tf16_reduced
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global dtype, dtype_vae, dtype_unet, unet_needs_upcast # pylint: disable=global-statement
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if shared.opts.cuda_dtype == 'FP16':
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dtype = dtype_vae = dtype_unet = torch.float16
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if shared.opts.cuda_dtype == 'BP16':
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dtype = dtype_vae = dtype_unet = torch.bfloat16
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if shared.opts.cuda_dtype == 'FP32':
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dtype = dtype_vae = dtype_unet = torch.float32
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unet_needs_upcast = shared.opts.upcast_sampling
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if shared.opts.cudnn_benchmark:
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torch.backends.cudnn.benchmark = True
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else:
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torch.backends.cudnn.benchmark = False
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errors.run(enable_tf32, "Enabling TF32")
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cpu = torch.device("cpu")
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device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
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@ -111,7 +113,7 @@ def autocast(disable=False):
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from modules import shared
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if disable:
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return contextlib.nullcontext()
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if dtype == torch.float32 or shared.cmd_opts.precision == "full":
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if dtype == torch.float32 or shared.cmd_opts.precision == "Full":
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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@ -1,22 +1,20 @@
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import datetime
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import pytz
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import io
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import math
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import os
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from collections import namedtuple
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import re
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import os
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import math
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import json
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import string
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import hashlib
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from collections import namedtuple
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import pytz
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import numpy as np
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import piexif
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import piexif.helper
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from PIL import Image, ImageFont, ImageDraw, PngImagePlugin, ExifTags
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import string
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import json
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import hashlib
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from modules import sd_samplers, shared, script_callbacks, errors
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from modules.shared import opts, cmd_opts
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from modules.shared import opts, cmd_opts # pylint: disable=unused-import
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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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@ -146,7 +144,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
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return ImageFont.truetype('javascript/roboto.ttf', fontsize)
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def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
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for i, line in enumerate(lines):
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for _i, line in enumerate(lines):
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fnt = initial_fnt
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fontsize = initial_fontsize
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while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
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@ -599,17 +597,14 @@ def safe_decode_string(s: bytes):
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remove_prefix = lambda text, prefix: text[len(prefix):] if text.startswith(prefix) else text
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for encoding in ['utf-8', 'utf-16', 'ascii', 'latin_1', 'cp1252', 'cp437']: # try different encodings
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try:
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decoded = s.decode(encoding, errors="strict")
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val = remove_prefix(decoded, 'UNICODE') # remove silly prefix added by old pnginfo/exif encoding
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val = remove_prefix(val, 'ASCII')
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s = remove_prefix(s, b'UNICODE')
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s = remove_prefix(s, b'ASCII')
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s = remove_prefix(s, b'\x00')
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val = s.decode(encoding, errors="strict")
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val = re.sub(r'[\x00-\x09]', '', val).strip() # remove remaining special characters
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if len(val) > 1024: # limit string length
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val = val[:1024]
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if len(val) == 0: # remove empty strings
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val = None
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# if not all(ord(c) < 128 for c in decoded): # only allow 7-bit ascii characters
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# val = None
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return val
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return val
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except:
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pass
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return None
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@ -651,7 +646,7 @@ Negative prompt: {json_info["uc"]}
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Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
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except Exception as e:
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errors.display(e, 'novelai image parser')
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print('GENINFO', geninfo)
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return geninfo, items
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@ -27,11 +27,11 @@ def download_default_clip_interrogate_categories(content_dir):
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print("Downloading CLIP categories...")
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tmpdir = content_dir + "_tmp"
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category_types = ["artists", "flavors", "mediums", "movements"]
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cat_types = ["artists", "flavors", "mediums", "movements"]
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try:
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os.makedirs(tmpdir)
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for category_type in category_types:
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for category_type in cat_types:
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torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
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os.rename(tmpdir, content_dir)
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@ -60,22 +60,21 @@ class InterrogateModels:
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download_default_clip_interrogate_categories(self.content_dir)
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if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
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return self.loaded_categories
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return self.loaded_categories
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self.loaded_categories = []
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if os.path.exists(self.content_dir):
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self.skip_categories = shared.opts.interrogate_clip_skip_categories
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category_types = []
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cat_types = []
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for filename in Path(self.content_dir).glob('*.txt'):
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category_types.append(filename.stem)
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cat_types.append(filename.stem)
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if filename.stem in self.skip_categories:
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continue
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m = re_topn.search(filename.stem)
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topn = 1 if m is None else int(m.group(1))
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with open(filename, "r", encoding="utf8") as file:
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lines = [x.strip() for x in file.readlines()]
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self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
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return self.loaded_categories
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@ -206,7 +205,7 @@ class InterrogateModels:
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image_features /= image_features.norm(dim=-1, keepdim=True)
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for name, topn, items in self.categories():
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for _name, topn, items in self.categories():
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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if shared.opts.interrogate_return_ranks:
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@ -49,6 +49,29 @@ for d, must_exist, what, options in path_dirs:
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paths[what] = d
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def create_paths(opts):
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def create_path(folder):
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if folder is None or folder == '':
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return
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if not os.path.exists(folder):
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print('Creating folder:', folder)
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os.makedirs(opts.temp_dir, exist_ok=True)
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create_path(opts.temp_dir)
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create_path(extensions_dir)
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create_path(extensions_builtin_dir)
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create_path(opts.ckpt_dir)
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create_path(opts.vae_dir)
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create_path(opts.embeddings_dir)
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create_path(opts.outdir_samples)
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create_path(opts.outdir_txt2img_samples)
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create_path(opts.outdir_img2img_samples)
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create_path(opts.outdir_extras_samples)
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create_path(opts.outdir_grids)
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create_path(opts.outdir_txt2img_grids)
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create_path(opts.outdir_img2img_grids)
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create_path(opts.outdir_save)
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class Prioritize:
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def __init__(self, name):
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self.name = name
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@ -2,19 +2,25 @@ import json
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import math
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import os
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import sys
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import random
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import logging
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from typing import Any, Dict, List
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import torch
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import numpy as np
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from PIL import Image, ImageFilter, ImageOps
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import random
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import cv2
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from skimage import exposure
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from typing import Any, Dict, List
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from ldm.data.util import AddMiDaS
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from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts # pylint: disable=unused-import
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from modules.sd_hijack import model_hijack
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from modules.shared import opts, cmd_opts, state
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from modules.shared import opts, cmd_opts, state # pylint: disable=unused-import
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import modules.shared as shared
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import modules.paths as paths
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import modules.face_restoration
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@ -22,12 +28,6 @@ import modules.images as images
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import modules.styles
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import modules.sd_models as sd_models
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import modules.sd_vae as sd_vae
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import logging
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from ldm.data.util import AddMiDaS
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from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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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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@ -198,7 +198,7 @@ class StableDiffusionProcessing:
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def unclip_image_conditioning(self, source_image):
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c_adm = self.sd_model.embedder(source_image)
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if self.sd_model.noise_augmentor is not None:
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noise_level = 0 # TODO: Allow other noise levels?
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noise_level = 0
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c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
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c_adm = torch.cat((c_adm, noise_level_emb), 1)
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return c_adm
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@ -267,7 +267,7 @@ class StableDiffusionProcessing:
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raise NotImplementedError()
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def close(self):
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self.sampler = None
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self.sampler = None # pylint: disable=attribute-defined-outside-init
|
||||
|
||||
|
||||
class Processed:
|
||||
|
|
@ -453,7 +453,7 @@ def fix_seed(p):
|
|||
p.subseed = get_fixed_seed(p.subseed)
|
||||
|
||||
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): # pylint: disable=unused-argument
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
|
||||
|
|
@ -527,7 +527,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
"""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"""
|
||||
|
||||
if type(p.prompt) == list:
|
||||
assert(len(p.prompt) > 0)
|
||||
assert len(p.prompt) > 0
|
||||
else:
|
||||
assert p.prompt is not None
|
||||
|
||||
|
|
@ -725,7 +725,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
if opts.return_mask:
|
||||
output_images.append(image_mask)
|
||||
|
||||
|
||||
if opts.return_mask_composite:
|
||||
output_images.append(image_mask_composite)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import sys
|
||||
from types import MethodType
|
||||
from rich import print # pylint: disable=redefined-builtin
|
||||
import torch
|
||||
|
|
@ -89,7 +88,6 @@ def undo_optimizations():
|
|||
def fix_checkpoint():
|
||||
"""checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
|
||||
checkpoints to be added when not training (there's a warning)"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
|
|
@ -173,16 +171,15 @@ class StableDiffusionModelHijack:
|
|||
if m.cond_stage_key == "edit":
|
||||
sd_hijack_unet.hijack_ddpm_edit()
|
||||
|
||||
"""
|
||||
try:
|
||||
import torch._dynamo as dynamo
|
||||
torch._dynamo.config.verbose = True
|
||||
torch.backends.cudnn.benchmark = True
|
||||
m.model = torch.compile(m.model, mode="default", backend="inductor", fullgraph=False, dynamic=False)
|
||||
print("Model compiled set")
|
||||
except Exception as err:
|
||||
print(f"Model compile not supported: {err}")
|
||||
"""
|
||||
if opts.cuda_compile and opts.cuda_compile_mode != 'none':
|
||||
try:
|
||||
import torch._dynamo as dynamo # pylint: disable=unused-import
|
||||
torch._dynamo.config.verbose = True # pylint: disable=protected-access
|
||||
torch.backends.cudnn.benchmark = True
|
||||
m.model = torch.compile(m.model, mode="default", backend=opts.cuda_compile_mode, fullgraph=False, dynamic=False)
|
||||
print("Model compile enabled:", opts.cuda_compile_mode)
|
||||
except Exception as err:
|
||||
print(f"Model compile not supported: {err}")
|
||||
|
||||
self.optimization_method = apply_optimizations()
|
||||
|
||||
|
|
|
|||
|
|
@ -299,11 +299,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
|
|||
if depth_model:
|
||||
model.depth_model = depth_model
|
||||
|
||||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||||
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
|
||||
devices.dtype_unet = model.model.diffusion_model.dtype
|
||||
devices.unet_needs_upcast = shared.opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
|
||||
|
||||
model.first_stage_model.to(devices.dtype_vae)
|
||||
|
||||
# clean up cache if limit is reached
|
||||
|
|
@ -403,7 +399,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
|||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
shared.sd_model = None
|
||||
|
||||
devices.enable_cudnn_benchmark()
|
||||
devices.set_cuda_params()
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
|
|
|
|||
|
|
@ -251,9 +251,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"cross_attention_optimization": OptionInfo("Scaled-Dot-Product", "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
|
||||
"cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
|
||||
"disable_nan_check": OptionInfo(False, "Do not check if produced images/latent spaces have NaN values"),
|
||||
"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
|
||||
"cudnn_benchmark": OptionInfo(False, "Enable CUDA cuDNN benchmark feature"),
|
||||
"sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size for the layer optimization to use", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
|
||||
"sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attentionkv chunk size for the sub-quadratic cross-attention layer optimization to use", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
|
||||
"sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention percentage of VRAM chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
|
||||
|
|
@ -323,6 +320,20 @@ options_templates.update(options_section(('saving-paths', "Image Paths"), {
|
|||
"outdir_save": OptionInfo("outputs/save", "Directory for saving images using the Save button", component_args=hide_dirs),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('cuda', "CUDA Settings"), {
|
||||
"precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
|
||||
"cuda_dtype": OptionInfo("FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
|
||||
"no_half": OptionInfo(False, "Use full precision for model (--no-half)"),
|
||||
"no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
|
||||
"disable_nan_check": OptionInfo(True, "Do not check if produced images/latent spaces have NaN values"),
|
||||
"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
|
||||
"cudnn_benchmark": OptionInfo(False, "Enable cuDNN benchmark feature"),
|
||||
"cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
|
||||
"cuda_allow_tf16_reduced": OptionInfo(True, "Allow TF16 reduced precision math ops"),
|
||||
"cuda_compile": OptionInfo(False, "Enable model compile (experimental)"),
|
||||
"cuda_compile_mode": OptionInfo("none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser']}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ from PIL import Image
|
|||
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
|
||||
|
||||
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing
|
||||
from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
|
||||
from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML # pylint: disable=unused-import
|
||||
from modules.paths import script_path, data_path
|
||||
from modules.shared import opts, cmd_opts
|
||||
import modules.codeformer_model
|
||||
|
|
@ -133,7 +133,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
|
|||
img = Image.open(image)
|
||||
filename = os.path.basename(image)
|
||||
left, _ = os.path.splitext(filename)
|
||||
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
|
||||
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a', encoding='utf-8'))
|
||||
|
||||
return [gr.update(), None]
|
||||
|
||||
|
|
@ -157,19 +157,19 @@ def create_seed_inputs(target_interface):
|
|||
random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
|
||||
|
||||
with FormRow(visible=True, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
|
||||
with FormRow(visible=True, elem_id=target_interface + '_subseed_row'):
|
||||
subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
|
||||
subseed.style(container=False)
|
||||
# random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
|
||||
random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
|
||||
reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
|
||||
subseed_strength = gr.Slider(label='Strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
|
||||
|
||||
with FormRow(visible=False) as seed_extra_row_2:
|
||||
with FormRow(visible=False):
|
||||
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
|
||||
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
|
||||
|
||||
random_seed.click(fn=lambda: [-1, -1], show_progress=False, inputs=[], outputs=[seed, subseed])
|
||||
# random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
|
||||
random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
|
||||
|
||||
return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w
|
||||
|
||||
|
|
@ -670,9 +670,9 @@ def create_ui():
|
|||
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
|
||||
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
gr.HTML(
|
||||
f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
||||
f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
|
||||
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
|
||||
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
||||
"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
|
||||
"<br>Add inpaint batch mask directory to enable inpaint batch processing."
|
||||
f"{hidden}</p>"
|
||||
)
|
||||
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
|
||||
|
|
@ -1162,8 +1162,8 @@ def create_ui():
|
|||
|
||||
with gr.Column(elem_id='ti_gallery_container'):
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
_ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
_ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
|
||||
create_embedding.click(
|
||||
|
|
|
|||
|
|
@ -30,10 +30,11 @@ def create_ui():
|
|||
script_inputs = scripts.scripts_postproc.setup_ui()
|
||||
|
||||
with gr.Column():
|
||||
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
|
||||
html2_info = gr.HTML()
|
||||
result_images, html_info_x, html_info, _html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
|
||||
html_info = gr.HTML(elem_id="pnginfo_html_info")
|
||||
generation_info = gr.Textbox(elem_id="pnginfo_generation_info", label="Parameters")
|
||||
gr.HTML('Full metadata')
|
||||
html2_info = gr.HTML(elem_id="pnginfo_html2_info")
|
||||
|
||||
for tabname, button in buttons.items():
|
||||
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=extras_image))
|
||||
|
|
|
|||
|
|
@ -51,9 +51,6 @@ gr.processing_utils.save_pil_to_file = save_pil_to_file
|
|||
def on_tmpdir_changed():
|
||||
if shared.opts.temp_dir == "":
|
||||
return
|
||||
if not os.path.isdir(shared.opts.temp_dir):
|
||||
print('Creating temporary folder:', shared.opts.temp_dir)
|
||||
os.makedirs(shared.opts.temp_dir, exist_ok=True)
|
||||
register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -233,9 +233,6 @@ button.custom-button{
|
|||
margin-right: 0;
|
||||
}
|
||||
|
||||
.performance .vram {
|
||||
}
|
||||
|
||||
#txt2img_generate, #img2img_generate {
|
||||
min-height: 4.5em;
|
||||
}
|
||||
|
|
|
|||
3
webui.py
3
webui.py
|
|
@ -27,6 +27,7 @@ import ldm.modules.encoders.modules # pylint: disable=W0611,C0411
|
|||
from modules import extra_networks, ui_extra_networks_checkpoints # pylint: disable=C0411,C0412
|
||||
from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
|
||||
from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call # pylint: disable=W0611,C0411
|
||||
from modules.paths import create_paths
|
||||
|
||||
# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
|
||||
if ".dev" in torch.__version__ or "+git" in torch.__version__:
|
||||
|
|
@ -152,8 +153,8 @@ def create_api(app):
|
|||
|
||||
def start_ui():
|
||||
logging.disable(logging.INFO)
|
||||
create_paths(opts)
|
||||
initialize()
|
||||
ui_tempdir.on_tmpdir_changed()
|
||||
if shared.opts.clean_temp_dir_at_start:
|
||||
ui_tempdir.cleanup_tmpdr()
|
||||
startup_timer.record("cleanup")
|
||||
|
|
|
|||
Loading…
Reference in New Issue