Merge branch 'dev' into master

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AUTOMATIC1111 2023-11-05 19:30:57 +03:00 committed by GitHub
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72 changed files with 1450 additions and 561 deletions

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@ -74,6 +74,7 @@ module.exports = {
create_submit_args: "readonly",
restart_reload: "readonly",
updateInput: "readonly",
onEdit: "readonly",
//extraNetworks.js
requestGet: "readonly",
popup: "readonly",

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@ -1,25 +1,45 @@
name: Bug Report
description: You think somethings is broken in the UI
description: You think something is broken in the UI
title: "[Bug]: "
labels: ["bug-report"]
body:
- type: checkboxes
attributes:
label: Is there an existing issue for this?
description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
options:
- label: I have searched the existing issues and checked the recent builds/commits
required: true
- type: markdown
attributes:
value: |
*Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
> The title of the bug report should be short and descriptive.
> Use relevant keywords for searchability.
> Do not leave it blank, but also do not put an entire error log in it.
- type: checkboxes
attributes:
label: Checklist
description: |
Please perform basic debugging to see if extensions or configuration is the cause of the issue.
Basic debug procedure
 1. Disable all third-party extensions - check if extension is the cause
 2. Update extensions and webui - sometimes things just need to be updated
 3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration
 4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed
 5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue
Before making a issue report please, check that the issue hasn't been reported recently.
options:
- label: The issue exists after disabling all extensions
- label: The issue exists on a clean installation of webui
- label: The issue is caused by an extension, but I believe it is caused by a bug in the webui
- label: The issue exists in the current version of the webui
- label: The issue has not been reported before recently
- label: The issue has been reported before but has not been fixed yet
- type: markdown
attributes:
value: |
> Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible
- type: textarea
id: what-did
attributes:
label: What happened?
description: Tell us what happened in a very clear and simple way
placeholder: |
txt2img is not working as intended.
validations:
required: true
- type: textarea
@ -27,9 +47,9 @@ body:
attributes:
label: Steps to reproduce the problem
description: Please provide us with precise step by step instructions on how to reproduce the bug
value: |
1. Go to ....
2. Press ....
placeholder: |
1. Go to ...
2. Press ...
3. ...
validations:
required: true
@ -38,13 +58,8 @@ body:
attributes:
label: What should have happened?
description: Tell us what you think the normal behavior should be
validations:
required: true
- type: textarea
id: sysinfo
attributes:
label: Sysinfo
description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
placeholder: |
WebUI should ...
validations:
required: true
- type: dropdown
@ -58,12 +73,25 @@ body:
- Brave
- Apple Safari
- Microsoft Edge
- Android
- iOS
- Other
- type: textarea
id: sysinfo
attributes:
label: Sysinfo
description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
placeholder: |
1. Go to WebUI Settings -> Sysinfo -> Download system info.
If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file
2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text.
validations:
required: true
- type: textarea
id: logs
attributes:
label: Console logs
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
render: Shell
validations:
required: true
@ -71,4 +99,7 @@ body:
id: misc
attributes:
label: Additional information
description: Please provide us with any relevant additional info or context.
description: |
Please provide us with any relevant additional info or context.
Examples:
 I have updated my GPU driver recently.

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@ -88,7 +88,7 @@ A browser interface based on Gradio library for Stable Diffusion.
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
- Now without any bad letters!
- Load checkpoints in safetensors format
- Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64
- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
@ -103,7 +103,7 @@ Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Installation on Windows 10/11 with NVidia-GPUs using release package
1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents.
2. Run `update.bat`.
3. Run `run.bat`.
> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)

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@ -0,0 +1,73 @@
model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 10000 ]
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: modules.xlmr_m18.BertSeriesModelWithTransformation
params:
name: "XLMR-Large"

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@ -0,0 +1,33 @@
import sys
import copy
import logging
class ColoredFormatter(logging.Formatter):
COLORS = {
"DEBUG": "\033[0;36m", # CYAN
"INFO": "\033[0;32m", # GREEN
"WARNING": "\033[0;33m", # YELLOW
"ERROR": "\033[0;31m", # RED
"CRITICAL": "\033[0;37;41m", # WHITE ON RED
"RESET": "\033[0m", # RESET COLOR
}
def format(self, record):
colored_record = copy.copy(record)
levelname = colored_record.levelname
seq = self.COLORS.get(levelname, self.COLORS["RESET"])
colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
return super().format(colored_record)
logger = logging.getLogger("lora")
logger.propagate = False
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
)
logger.addHandler(handler)

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@ -93,6 +93,7 @@ class Network: # LoraModule
self.unet_multiplier = 1.0
self.dyn_dim = None
self.modules = {}
self.bundle_embeddings = {}
self.mtime = None
self.mentioned_name = None

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@ -0,0 +1,33 @@
import network
class ModuleTypeGLora(network.ModuleType):
def create_module(self, net: network.Network, weights: network.NetworkWeights):
if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
return NetworkModuleGLora(net, weights)
return None
# adapted from https://github.com/KohakuBlueleaf/LyCORIS
class NetworkModuleGLora(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
if hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
self.w1a = weights.w["a1.weight"]
self.w1b = weights.w["b1.weight"]
self.w2a = weights.w["a2.weight"]
self.w2b = weights.w["b2.weight"]
def calc_updown(self, orig_weight):
w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
output_shape = [w1a.size(0), w1b.size(1)]
updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
return self.finalize_updown(updown, orig_weight, output_shape)

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@ -5,6 +5,7 @@ import re
import lora_patches
import network
import network_lora
import network_glora
import network_hada
import network_ia3
import network_lokr
@ -15,6 +16,9 @@ import torch
from typing import Union
from modules import shared, devices, sd_models, errors, scripts, sd_hijack
import modules.textual_inversion.textual_inversion as textual_inversion
from lora_logger import logger
module_types = [
network_lora.ModuleTypeLora(),
@ -23,6 +27,7 @@ module_types = [
network_lokr.ModuleTypeLokr(),
network_full.ModuleTypeFull(),
network_norm.ModuleTypeNorm(),
network_glora.ModuleTypeGLora(),
]
@ -149,9 +154,19 @@ def load_network(name, network_on_disk):
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
matched_networks = {}
bundle_embeddings = {}
for key_network, weight in sd.items():
key_network_without_network_parts, network_part = key_network.split(".", 1)
if key_network_without_network_parts == "bundle_emb":
emb_name, vec_name = network_part.split(".", 1)
emb_dict = bundle_embeddings.get(emb_name, {})
if vec_name.split('.')[0] == 'string_to_param':
_, k2 = vec_name.split('.', 1)
emb_dict['string_to_param'] = {k2: weight}
else:
emb_dict[vec_name] = weight
bundle_embeddings[emb_name] = emb_dict
key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@ -195,6 +210,14 @@ def load_network(name, network_on_disk):
net.modules[key] = net_module
embeddings = {}
for emb_name, data in bundle_embeddings.items():
embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
embedding.loaded = None
embeddings[emb_name] = embedding
net.bundle_embeddings = embeddings
if keys_failed_to_match:
logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
@ -210,11 +233,15 @@ def purge_networks_from_memory():
def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
emb_db = sd_hijack.model_hijack.embedding_db
already_loaded = {}
for net in loaded_networks:
if net.name in names:
already_loaded[net.name] = net
for emb_name, embedding in net.bundle_embeddings.items():
if embedding.loaded:
emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)
loaded_networks.clear()
@ -257,6 +284,21 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
loaded_networks.append(net)
for emb_name, embedding in net.bundle_embeddings.items():
if embedding.loaded is None and emb_name in emb_db.word_embeddings:
logger.warning(
f'Skip bundle embedding: "{emb_name}"'
' as it was already loaded from embeddings folder'
)
continue
embedding.loaded = False
if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
embedding.loaded = True
emb_db.register_embedding(embedding, shared.sd_model)
else:
emb_db.skipped_embeddings[name] = embedding
if failed_to_load_networks:
sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
@ -418,6 +460,7 @@ def network_forward(module, input, original_forward):
def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
self.network_current_names = ()
self.network_weights_backup = None
self.network_bias_backup = None
def network_Linear_forward(self, input):
@ -564,6 +607,7 @@ extra_network_lora = None
available_networks = {}
available_network_aliases = {}
loaded_networks = []
loaded_bundle_embeddings = {}
networks_in_memory = {}
available_network_hash_lookup = {}
forbidden_network_aliases = {}

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@ -12,6 +12,8 @@ function isMobile() {
}
function reportWindowSize() {
if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout
var currentlyMobile = isMobile();
if (currentlyMobile == isSetupForMobile) return;
isSetupForMobile = currentlyMobile;

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@ -119,7 +119,7 @@ window.addEventListener('paste', e => {
}
const firstFreeImageField = visibleImageFields
.filter(el => el.querySelector('input[type=file]'))?.[0];
.filter(el => !el.querySelector('img'))?.[0];
dropReplaceImage(
firstFreeImageField ?

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@ -18,37 +18,43 @@ function keyupEditAttention(event) {
const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN);
if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
}
let beforeClosingParen = before.lastIndexOf(CLOSE);
if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false;
// Find closing parenthesis around current cursor
const after = text.substring(selectionStart);
let afterParen = after.indexOf(CLOSE);
if (afterParen == -1) return false;
let afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1);
afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
}
if (beforeParen === -1 || afterParen === -1) return false;
let afterOpeningParen = after.indexOf(OPEN);
if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false;
// Set the selection to the text between the parenthesis
const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
const lastColon = parenContent.lastIndexOf(":");
selectionStart = beforeParen + 1;
selectionEnd = selectionStart + lastColon;
if (/.*:-?[\d.]+/s.test(parenContent)) {
const lastColon = parenContent.lastIndexOf(":");
selectionStart = beforeParen + 1;
selectionEnd = selectionStart + lastColon;
} else {
selectionStart = beforeParen + 1;
selectionEnd = selectionStart + parenContent.length;
}
target.setSelectionRange(selectionStart, selectionEnd);
return true;
}
function selectCurrentWord() {
if (selectionStart !== selectionEnd) return false;
const delimiters = opts.keyedit_delimiters + " \r\n\t";
const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"};
let delimiters = opts.keyedit_delimiters;
// seek backward until to find beggining
for (let i of opts.keyedit_delimiters_whitespace) {
delimiters += whitespace_delimiters[i];
}
// seek backward to find beginning
while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
selectionStart--;
}
@ -63,7 +69,7 @@ function keyupEditAttention(event) {
}
// If the user hasn't selected anything, let's select their current parenthesis block or word
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) {
selectCurrentWord();
}
@ -71,33 +77,54 @@ function keyupEditAttention(event) {
var closeCharacter = ')';
var delta = opts.keyedit_precision_attention;
var start = selectionStart > 0 ? text[selectionStart - 1] : "";
var end = text[selectionEnd];
if (selectionStart > 0 && text[selectionStart - 1] == '<') {
if (start == '<') {
closeCharacter = '>';
delta = opts.keyedit_precision_extra;
} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
} else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis)))
let numParen = 0;
while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) {
numParen++;
}
if (start == "[") {
weight = (1 / 1.1) ** numParen;
} else {
weight = 1.1 ** numParen;
}
weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention;
text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen);
selectionStart -= numParen - 1;
selectionEnd -= numParen - 1;
} else if (start != '(') {
// do not include spaces at the end
while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
selectionEnd -= 1;
selectionEnd--;
}
if (selectionStart == selectionEnd) {
return;
}
text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
selectionStart += 1;
selectionEnd += 1;
selectionStart++;
selectionEnd++;
}
var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
if (text[selectionEnd] != ':') return;
var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength));
if (isNaN(weight)) return;
weight += isPlus ? delta : -delta;
weight = parseFloat(weight.toPrecision(12));
if (String(weight).length == 1) weight += ".0";
if (Number.isInteger(weight)) weight += ".0";
if (closeCharacter == ')' && weight == 1) {
var endParenPos = text.substring(selectionEnd).indexOf(')');
@ -105,7 +132,7 @@ function keyupEditAttention(event) {
selectionStart--;
selectionEnd--;
} else {
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength);
}
target.focus();

View File

@ -26,8 +26,9 @@ function setupExtraNetworksForTab(tabname) {
var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container');
var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt');
sort.dataset.sortkey = 'sortDefault';
tabs.appendChild(searchDiv);
tabs.appendChild(sort);
tabs.appendChild(sortOrder);
@ -49,20 +50,23 @@ function setupExtraNetworksForTab(tabname) {
elem.style.display = visible ? "" : "none";
});
applySort();
};
var applySort = function() {
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
var reverse = sortOrder.classList.contains("sortReverse");
var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name";
sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
if (sortKeyStore == sort.dataset.sortkey) {
return;
}
sort.dataset.sortkey = sortKeyStore;
var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
cards.forEach(function(card) {
card.originalParentElement = card.parentElement;
});
@ -88,15 +92,13 @@ function setupExtraNetworksForTab(tabname) {
};
search.addEventListener("input", applyFilter);
applyFilter();
["change", "blur", "click"].forEach(function(evt) {
sort.querySelector("input").addEventListener(evt, applySort);
});
sortOrder.addEventListener("click", function() {
sortOrder.classList.toggle("sortReverse");
applySort();
});
applyFilter();
extraNetworksApplySort[tabname] = applySort;
extraNetworksApplyFilter[tabname] = applyFilter;
var showDirsUpdate = function() {
@ -109,11 +111,47 @@ function setupExtraNetworksForTab(tabname) {
showDirsUpdate();
}
function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) {
if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout
var promptContainer = gradioApp().getElementById(tabname + '_prompt_container');
var prompt = gradioApp().getElementById(tabname + '_prompt_row');
var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row');
var elem = id ? gradioApp().getElementById(id) : null;
if (showNegativePrompt && elem) {
elem.insertBefore(negPrompt, elem.firstChild);
} else {
promptContainer.insertBefore(negPrompt, promptContainer.firstChild);
}
if (showPrompt && elem) {
elem.insertBefore(prompt, elem.firstChild);
} else {
promptContainer.insertBefore(prompt, promptContainer.firstChild);
}
}
function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
extraNetworksMovePromptToTab(tabname, '', false, false);
}
function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
}
function applyExtraNetworkFilter(tabname) {
setTimeout(extraNetworksApplyFilter[tabname], 1);
}
function applyExtraNetworkSort(tabname) {
setTimeout(extraNetworksApplySort[tabname], 1);
}
var extraNetworksApplyFilter = {};
var extraNetworksApplySort = {};
var activePromptTextarea = {};
function setupExtraNetworks() {
@ -140,14 +178,15 @@ function setupExtraNetworks() {
onUiLoaded(setupExtraNetworks);
var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
var m = text.match(re_extranet);
var replaced = false;
var newTextareaText;
if (m) {
var extraTextBeforeNet = opts.extra_networks_add_text_separator;
var extraTextAfterNet = m[2];
var partToSearch = m[1];
var foundAtPosition = -1;
@ -161,8 +200,13 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
return found;
});
if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
if (foundAtPosition >= 0) {
if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
}
if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition);
}
}
} else {
newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
@ -216,27 +260,24 @@ function extraNetworksSearchButton(tabs_id, event) {
var globalPopup = null;
var globalPopupInner = null;
function closePopup() {
if (!globalPopup) return;
globalPopup.style.display = "none";
}
function popup(contents) {
if (!globalPopup) {
globalPopup = document.createElement('div');
globalPopup.onclick = closePopup;
globalPopup.classList.add('global-popup');
var close = document.createElement('div');
close.classList.add('global-popup-close');
close.onclick = closePopup;
close.addEventListener("click", closePopup);
close.title = "Close";
globalPopup.appendChild(close);
globalPopupInner = document.createElement('div');
globalPopupInner.onclick = function(event) {
event.stopPropagation(); return false;
};
globalPopupInner.classList.add('global-popup-inner');
globalPopup.appendChild(globalPopupInner);
@ -335,7 +376,7 @@ function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
function extraNetworksRefreshSingleCard(page, tabname, name) {
requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
if (data && data.html) {
var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`);
var newDiv = document.createElement('DIV');
newDiv.innerHTML = data.html;

View File

@ -33,8 +33,11 @@ function updateOnBackgroundChange() {
const modalImage = gradioApp().getElementById("modalImage");
if (modalImage && modalImage.offsetParent) {
let currentButton = selected_gallery_button();
if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
let preview = gradioApp().querySelectorAll('.livePreview > img');
if (preview.length > 0) {
// show preview image if available
modalImage.src = preview[preview.length - 1].src;
} else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
modalImage.src = currentButton.children[0].src;
if (modalImage.style.display === 'none') {
const modal = gradioApp().getElementById("lightboxModal");

View File

@ -1,37 +1,68 @@
var observerAccordionOpen = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
var elem = mutationRecord.target;
var open = elem.classList.contains('open');
var accordion = elem.parentNode;
accordion.classList.toggle('input-accordion-open', open);
var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
checkbox.checked = open;
updateInput(checkbox);
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
if (extra) {
extra.style.display = open ? "" : "none";
}
});
});
function inputAccordionChecked(id, checked) {
var label = gradioApp().querySelector('#' + id + " .label-wrap");
if (label.classList.contains('open') != checked) {
label.click();
var accordion = gradioApp().getElementById(id);
accordion.visibleCheckbox.checked = checked;
accordion.onVisibleCheckboxChange();
}
function setupAccordion(accordion) {
var labelWrap = accordion.querySelector('.label-wrap');
var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
var span = labelWrap.querySelector('span');
var linked = true;
var isOpen = function() {
return labelWrap.classList.contains('open');
};
var observerAccordionOpen = new MutationObserver(function(mutations) {
mutations.forEach(function(mutationRecord) {
accordion.classList.toggle('input-accordion-open', isOpen());
if (linked) {
accordion.visibleCheckbox.checked = isOpen();
accordion.onVisibleCheckboxChange();
}
});
});
observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
if (extra) {
labelWrap.insertBefore(extra, labelWrap.lastElementChild);
}
accordion.onChecked = function(checked) {
if (isOpen() != checked) {
labelWrap.click();
}
};
var visibleCheckbox = document.createElement('INPUT');
visibleCheckbox.type = 'checkbox';
visibleCheckbox.checked = isOpen();
visibleCheckbox.id = accordion.id + "-visible-checkbox";
visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
span.insertBefore(visibleCheckbox, span.firstChild);
accordion.visibleCheckbox = visibleCheckbox;
accordion.onVisibleCheckboxChange = function() {
if (linked && isOpen() != visibleCheckbox.checked) {
labelWrap.click();
}
gradioCheckbox.checked = visibleCheckbox.checked;
updateInput(gradioCheckbox);
};
visibleCheckbox.addEventListener('click', function(event) {
linked = false;
event.stopPropagation();
});
visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange);
}
onUiLoaded(function() {
for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
var labelWrap = accordion.querySelector('.label-wrap');
observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
if (extra) {
labelWrap.insertBefore(extra, labelWrap.lastElementChild);
}
setupAccordion(accordion);
}
});

46
javascript/settings.js Normal file
View File

@ -0,0 +1,46 @@
let settingsExcludeTabsFromShowAll = {
settings_tab_defaults: 1,
settings_tab_sysinfo: 1,
settings_tab_actions: 1,
settings_tab_licenses: 1,
};
function settingsShowAllTabs() {
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
if (settingsExcludeTabsFromShowAll[elem.id]) return;
elem.style.display = "block";
});
}
function settingsShowOneTab() {
gradioApp().querySelector('#settings_show_one_page').click();
}
onUiLoaded(function() {
var edit = gradioApp().querySelector('#settings_search');
var editTextarea = gradioApp().querySelector('#settings_search > label > input');
var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages');
var settings_tabs = gradioApp().querySelector('#settings div');
onEdit('settingsSearch', editTextarea, 250, function() {
var searchText = (editTextarea.value || "").trim().toLowerCase();
gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) {
var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1;
elem.style.display = visible ? "" : "none";
});
if (searchText != "") {
settingsShowAllTabs();
} else {
settingsShowOneTab();
}
});
settings_tabs.insertBefore(edit, settings_tabs.firstChild);
settings_tabs.appendChild(buttonShowAllPages);
buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
});

View File

@ -1,10 +1,9 @@
let promptTokenCountDebounceTime = 800;
let promptTokenCountTimeouts = {};
var promptTokenCountUpdateFunctions = {};
let promptTokenCountUpdateFunctions = {};
function update_txt2img_tokens(...args) {
// Called from Gradio
update_token_counter("txt2img_token_button");
update_token_counter("txt2img_negative_token_button");
if (args.length == 2) {
return args[0];
}
@ -14,6 +13,7 @@ function update_txt2img_tokens(...args) {
function update_img2img_tokens(...args) {
// Called from Gradio
update_token_counter("img2img_token_button");
update_token_counter("img2img_negative_token_button");
if (args.length == 2) {
return args[0];
}
@ -21,16 +21,7 @@ function update_img2img_tokens(...args) {
}
function update_token_counter(button_id) {
if (opts.disable_token_counters) {
return;
}
if (promptTokenCountTimeouts[button_id]) {
clearTimeout(promptTokenCountTimeouts[button_id]);
}
promptTokenCountTimeouts[button_id] = setTimeout(
() => gradioApp().getElementById(button_id)?.click(),
promptTokenCountDebounceTime,
);
promptTokenCountUpdateFunctions[button_id]?.();
}
@ -69,10 +60,11 @@ function setupTokenCounting(id, id_counter, id_button) {
prompt.parentElement.insertBefore(counter, prompt);
prompt.parentElement.style.position = "relative";
promptTokenCountUpdateFunctions[id] = function() {
update_token_counter(id_button);
};
textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
var func = onEdit(id, textarea, 800, function() {
gradioApp().getElementById(id_button)?.click();
});
promptTokenCountUpdateFunctions[id] = func;
promptTokenCountUpdateFunctions[id_button] = func;
}
function setupTokenCounters() {

View File

@ -263,21 +263,6 @@ onAfterUiUpdate(function() {
json_elem.parentElement.style.display = "none";
setupTokenCounters();
var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
var settings_tabs = gradioApp().querySelector('#settings div');
if (show_all_pages && settings_tabs) {
settings_tabs.appendChild(show_all_pages);
show_all_pages.onclick = function() {
gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
if (elem.id == "settings_tab_licenses") {
return;
}
elem.style.display = "block";
});
};
}
});
onOptionsChanged(function() {
@ -366,3 +351,20 @@ function switchWidthHeight(tabname) {
updateInput(height);
return [];
}
var onEditTimers = {};
// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
function onEdit(editId, elem, afterMs, func) {
var edited = function() {
var existingTimer = onEditTimers[editId];
if (existingTimer) clearTimeout(existingTimer);
onEditTimers[editId] = setTimeout(func, afterMs);
};
elem.addEventListener("input", edited);
return edited;
}

View File

@ -17,19 +17,18 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
from modules.api import models
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
from typing import Dict, List, Any
from typing import Any
import piexif
import piexif.helper
from contextlib import closing
@ -103,7 +102,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png':
use_metadata = False
metadata = PngImagePlugin.PngInfo()
@ -221,15 +221,15 @@ class Api:
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
@ -242,7 +242,8 @@ class Api:
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -473,9 +474,6 @@ class Api:
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip())
if image is None:
return models.PNGInfoResponse(info="")
@ -484,9 +482,10 @@ class Api:
if geninfo is None:
geninfo = ""
items = {**{'parameters': geninfo}, **items}
params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
script_callbacks.infotext_pasted_callback(geninfo, params)
return models.PNGInfoResponse(info=geninfo, items=items)
return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
def progressapi(self, req: models.ProgressRequest = Depends()):
# copy from check_progress_call of ui.py
@ -541,12 +540,12 @@ class Api:
return {}
def unloadapi(self):
unload_model_weights()
sd_models.unload_model_weights()
return {}
def reloadapi(self):
reload_model_weights()
sd_models.send_model_to_device(shared.sd_model)
return {}
@ -564,9 +563,9 @@ class Api:
return options
def set_config(self, req: Dict[str, Any]):
def set_config(self, req: dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found")
for k, v in req.items():
@ -770,6 +769,25 @@ class Api:
cuda = {'error': f'{err}'}
return models.MemoryResponse(ram=ram, cuda=cuda)
def get_extensions_list(self):
from modules import extensions
extensions.list_extensions()
ext_list = []
for ext in extensions.extensions:
ext: extensions.Extension
ext.read_info_from_repo()
if ext.remote is not None:
ext_list.append({
"name": ext.name,
"remote": ext.remote,
"branch": ext.branch,
"commit_hash":ext.commit_hash,
"commit_date":ext.commit_date,
"version":ext.version,
"enabled":ext.enabled
})
return ext_list
def launch(self, server_name, port, root_path):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)

View File

@ -1,12 +1,10 @@
import inspect
from pydantic import BaseModel, Field, create_model
from typing import Any, Optional
from typing_extensions import Literal
from typing import Any, Optional, Literal
from inflection import underscore
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
from modules.shared import sd_upscalers, opts, parser
from typing import Dict, List
API_NOT_ALLOWED = [
"self",
@ -130,12 +128,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
).generate_model()
class TextToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
class ImageToImageResponse(BaseModel):
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: dict
info: str
@ -168,17 +166,18 @@ class FileData(BaseModel):
name: str = Field(title="File name")
class ExtrasBatchImagesRequest(ExtrasBaseRequest):
imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
class ExtrasBatchImagesResponse(ExtraBaseResponse):
images: List[str] = Field(title="Images", description="The generated images in base64 format.")
images: list[str] = Field(title="Images", description="The generated images in base64 format.")
class PNGInfoRequest(BaseModel):
image: str = Field(title="Image", description="The base64 encoded PNG image")
class PNGInfoResponse(BaseModel):
info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
items: dict = Field(title="Items", description="An object containing all the info the image had")
items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
class ProgressRequest(BaseModel):
skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
@ -232,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)
class SamplerItem(BaseModel):
name: str = Field(title="Name")
aliases: List[str] = Field(title="Aliases")
options: Dict[str, str] = Field(title="Options")
aliases: list[str] = Field(title="Aliases")
options: dict[str, str] = Field(title="Options")
class UpscalerItem(BaseModel):
name: str = Field(title="Name")
@ -284,8 +283,8 @@ class EmbeddingItem(BaseModel):
vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
class EmbeddingsResponse(BaseModel):
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
class MemoryResponse(BaseModel):
ram: dict = Field(title="RAM", description="System memory stats")
@ -303,11 +302,20 @@ class ScriptArg(BaseModel):
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
class ScriptInfo(BaseModel):
name: str = Field(default=None, title="Name", description="Script name")
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
class ExtensionItem(BaseModel):
name: str = Field(title="Name", description="Extension name")
remote: str = Field(title="Remote", description="Extension Repository URL")
branch: str = Field(title="Branch", description="Extension Repository Branch")
commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
version: str = Field(title="Version", description="Extension Version")
commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")

View File

@ -90,7 +90,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts
parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
@ -107,13 +107,14 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions")
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
parser.add_argument('--add-stop-route', action='store_true', help='does not do anything')
parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)
parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )

View File

@ -4,7 +4,6 @@ Supports saving and restoring webui and extensions from a known working set of c
import os
import json
import time
import tqdm
from datetime import datetime
@ -38,7 +37,7 @@ def list_config_states():
config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
for cs in config_states:
timestamp = time.asctime(time.gmtime(cs["created_at"]))
timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
name = cs.get("name", "Config")
full_name = f"{name}: {timestamp}"
all_config_states[full_name] = cs

View File

@ -60,7 +60,8 @@ def enable_tf32():
# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True

View File

@ -9,7 +9,7 @@ from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks, processing
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")

View File

@ -9,6 +9,7 @@ from modules import paths, shared, devices, modelloader, errors
model_dir = "GFPGAN"
user_path = None
model_path = os.path.join(paths.models_path, model_dir)
model_file_path = None
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
have_gfpgan = False
loaded_gfpgan_model = None
@ -17,6 +18,7 @@ loaded_gfpgan_model = None
def gfpgann():
global loaded_gfpgan_model
global model_path
global model_file_path
if loaded_gfpgan_model is not None:
loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
return loaded_gfpgan_model
@ -24,17 +26,24 @@ def gfpgann():
if gfpgan_constructor is None:
return None
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
if len(models) == 1 and models[0].startswith("http"):
model_file = models[0]
elif len(models) != 0:
latest_file = max(models, key=os.path.getctime)
gfp_models = []
for item in models:
if 'GFPGAN' in os.path.basename(item):
gfp_models.append(item)
latest_file = max(gfp_models, key=os.path.getctime)
model_file = latest_file
else:
print("Unable to load gfpgan model!")
return None
if hasattr(facexlib.detection.retinaface, 'device'):
facexlib.detection.retinaface.device = devices.device_gfpgan
model_file_path = model_file
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
loaded_gfpgan_model = model
@ -77,19 +86,25 @@ def setup_model(dirname):
global user_path
global have_gfpgan
global gfpgan_constructor
global model_file_path
facexlib_path = model_path
if dirname is not None:
facexlib_path = dirname
load_file_from_url_orig = gfpgan.utils.load_file_from_url
facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
def my_load_file_from_url(**kwargs):
return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))
def facex_load_file_from_url(**kwargs):
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
def facex_load_file_from_url2(**kwargs):
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
gfpgan.utils.load_file_from_url = my_load_file_from_url
facexlib.detection.load_file_from_url = facex_load_file_from_url

View File

@ -23,7 +23,7 @@ class Git(git.Git):
)
return self._parse_object_header(ret)
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
# Not really streaming, per se; this buffers the entire object in memory.
# Shouldn't be a problem for our use case, since we're only using this for
# object headers (commit objects).

View File

@ -468,7 +468,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
shared.reload_hypernetworks()
def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int):
from modules import images, processing
save_hypernetwork_every = save_hypernetwork_every or 0
@ -698,7 +698,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

View File

@ -561,6 +561,8 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
})
piexif.insert(exif_bytes, filename)
elif extension.lower() == ".gif":
image.save(filename, format=image_format, comment=geninfo)
else:
image.save(filename, format=image_format, quality=opts.jpeg_quality)
@ -661,7 +663,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
os.replace(temp_file_path, filename_without_extension + extension)
filename = filename_without_extension + extension
if shared.opts.save_images_replace_action != "Replace":
n = 0
while os.path.exists(filename):
n += 1
filename = f"{filename_without_extension}-{n}{extension}"
os.replace(temp_file_path, filename)
fullfn_without_extension, extension = os.path.splitext(params.filename)
if hasattr(os, 'statvfs'):
@ -718,7 +726,12 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
geninfo = items.pop('parameters', None)
if "exif" in items:
exif = piexif.load(items["exif"])
exif_data = items["exif"]
try:
exif = piexif.load(exif_data)
except OSError:
# memory / exif was not valid so piexif tried to read from a file
exif = None
exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
try:
exif_comment = piexif.helper.UserComment.load(exif_comment)
@ -728,6 +741,8 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
if exif_comment:
items['exif comment'] = exif_comment
geninfo = exif_comment
elif "comment" in items: # for gif
geninfo = items["comment"].decode('utf8', errors="ignore")
for field in IGNORED_INFO_KEYS:
items.pop(field, None)

View File

@ -10,6 +10,7 @@ from modules import images as imgutil
from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
from modules.sd_models import get_closet_checkpoint_match
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
@ -41,7 +42,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
cfg_scale = p.cfg_scale
sampler_name = p.sampler_name
steps = p.steps
override_settings = p.override_settings
sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
@ -104,15 +106,27 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
p.steps = int(parsed_parameters.get("Steps", steps))
model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
if model_info is not None:
p.override_settings['sd_model_checkpoint'] = model_info.name
elif sd_model_checkpoint_override:
p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
else:
p.override_settings.pop("sd_model_checkpoint", None)
if output_dir:
p.outpath_samples = output_dir
p.override_settings['save_to_dirs'] = False
p.override_settings['save_images_replace_action'] = "Add number suffix"
if p.n_iter > 1 or p.batch_size > 1:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
else:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
if output_dir:
p.outpath_samples = output_dir
p.override_settings['save_to_dirs'] = False
if p.n_iter > 1 or p.batch_size > 1:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
else:
p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
p.override_settings.pop('save_images_replace_action', None)
process_images(p)
@ -189,7 +203,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
p.user = request.username
if shared.cmd_opts.enable_console_prompts:
if shared.opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
if mask:

View File

@ -151,8 +151,8 @@ def initialize_rest(*, reload_script_modules=False):
from modules import devices
devices.first_time_calculation()
Thread(target=load_model).start()
if not shared.cmd_opts.skip_load_model_at_start:
Thread(target=load_model).start()
from modules import shared_items
shared_items.reload_hypernetworks()

View File

@ -150,10 +150,14 @@ def dumpstacks():
def configure_sigint_handler():
# make the program just exit at ctrl+c without waiting for anything
from modules import shared
def sigint_handler(sig, frame):
print(f'Interrupted with signal {sig} in {frame}')
dumpstacks()
if shared.opts.dump_stacks_on_signal:
dumpstacks()
os._exit(0)

View File

@ -64,7 +64,7 @@ Use --skip-python-version-check to suppress this warning.
@lru_cache()
def commit_hash():
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
return subprocess.check_output([git, "-C", script_path, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
@ -72,7 +72,7 @@ def commit_hash():
@lru_cache()
def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
return subprocess.check_output([git, "-C", script_path, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
try:

View File

@ -14,21 +14,24 @@ def list_localizations(dirname):
if ext.lower() != ".json":
continue
localizations[fn] = os.path.join(dirname, file)
localizations[fn] = [os.path.join(dirname, file)]
for file in scripts.list_scripts("localizations", ".json"):
fn, ext = os.path.splitext(file.filename)
localizations[fn] = file.path
if fn not in localizations:
localizations[fn] = []
localizations[fn].append(file.path)
def localization_js(current_localization_name: str) -> str:
fn = localizations.get(current_localization_name, None)
fns = localizations.get(current_localization_name, None)
data = {}
if fn is not None:
try:
with open(fn, "r", encoding="utf8") as file:
data = json.load(file)
except Exception:
errors.report(f"Error loading localization from {fn}", exc_info=True)
if fns is not None:
for fn in fns:
try:
with open(fn, "r", encoding="utf8") as file:
data.update(json.load(file))
except Exception:
errors.report(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"

View File

@ -210,6 +210,8 @@ class Options:
def add_option(self, key, info):
self.data_labels[key] = info
if key not in self.data:
self.data[key] = info.default
def reorder(self):
"""reorder settings so that all items related to section always go together"""

View File

@ -1,6 +1,6 @@
import os
import sys
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir # noqa: F401
from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, cwd # noqa: F401
import modules.safe # noqa: F401

View File

@ -8,6 +8,7 @@ import shlex
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)
cwd = os.getcwd()
modules_path = os.path.dirname(os.path.realpath(__file__))
script_path = os.path.dirname(modules_path)

View File

@ -142,7 +142,7 @@ class StableDiffusionProcessing:
overlay_images: list = None
eta: float = None
do_not_reload_embeddings: bool = False
denoising_strength: float = 0
denoising_strength: float = None
ddim_discretize: str = None
s_min_uncond: float = None
s_churn: float = None
@ -533,6 +533,7 @@ class Processed:
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
self.version = program_version()
def js(self):
obj = {
@ -567,6 +568,7 @@ class Processed:
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
"version": self.version,
}
return json.dumps(obj)
@ -709,7 +711,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@ -884,6 +886,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
state.nextjob()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -956,7 +960,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
state.nextjob()
if not infotexts:
infotexts.append(Processed(p, []).infotext(p, 0))
p.color_corrections = None

View File

@ -29,8 +29,8 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
else:
self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), tooltip="Set seed to -1, which will cause a new random number to be used every time")
reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), tooltip="Reuse seed from last generation, mostly useful if it was randomized")
seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)

View File

@ -2,10 +2,9 @@ from __future__ import annotations
import re
from collections import namedtuple
from typing import List
import lark
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@ -240,14 +239,14 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
class ComposableScheduledPromptConditioning:
def __init__(self, schedules, weight=1.0):
self.schedules: List[ScheduledPromptConditioning] = schedules
self.schedules: list[ScheduledPromptConditioning] = schedules
self.weight: float = weight
class MulticondLearnedConditioning:
def __init__(self, shape, batch):
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
@ -278,7 +277,7 @@ class DictWithShape(dict):
return self["crossattn"].shape
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
param = c[0][0].cond
is_dict = isinstance(param, dict)

View File

@ -14,7 +14,9 @@ def is_restartable() -> bool:
def restart_program() -> None:
"""creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
(Path(script_path) / "tmp" / "restart").touch()
tmpdir = Path(script_path) / "tmp"
tmpdir.mkdir(parents=True, exist_ok=True)
(tmpdir / "restart").touch()
stop_program()

View File

@ -1,7 +1,7 @@
import inspect
import os
from collections import namedtuple
from typing import Optional, Dict, Any
from typing import Optional, Any
from fastapi import FastAPI
from gradio import Blocks
@ -258,7 +258,7 @@ def image_grid_callback(params: ImageGridLoopParams):
report_exception(c, 'image_grid')
def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
for c in callback_map['callbacks_infotext_pasted']:
try:
c.callback(infotext, params)
@ -449,7 +449,7 @@ def on_infotext_pasted(callback):
"""register a function to be called before applying an infotext.
The callback is called with two arguments:
- infotext: str - raw infotext.
- result: Dict[str, any] - parsed infotext parameters.
- result: dict[str, any] - parsed infotext parameters.
"""
add_callback(callback_map['callbacks_infotext_pasted'], callback)

View File

@ -491,11 +491,15 @@ class ScriptRunner:
arg_info = api_models.ScriptArg(label=control.label or "")
for field in ("value", "minimum", "maximum", "step", "choices"):
for field in ("value", "minimum", "maximum", "step"):
v = getattr(control, field, None)
if v is not None:
setattr(arg_info, field, v)
choices = getattr(control, 'choices', None) # as of gradio 3.41, some items in choices are strings, and some are tuples where the first elem is the string
if choices is not None:
arg_info.choices = [x[0] if isinstance(x, tuple) else x for x in choices]
api_args.append(arg_info)
script.api_info = api_models.ScriptInfo(

View File

@ -2,14 +2,15 @@ import torch
from torch.nn.functional import silu
from types import MethodType
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
from modules.hypernetworks import hypernetwork
from modules.shared import cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
import ldm.modules.diffusionmodules.openaimodel
import ldm.models.diffusion.ddpm
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
import ldm.modules.encoders.modules
@ -37,6 +38,8 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
optimizers = []
current_optimizer: sd_hijack_optimizations.SdOptimization = None
ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
def list_optimizers():
new_optimizers = script_callbacks.list_optimizers_callback()
@ -220,7 +223,7 @@ class StableDiffusionModelHijack:
else:
m.cond_stage_model = conditioner
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
model_embeddings = m.cond_stage_model.roberta.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
@ -251,10 +254,13 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
sd_unet.original_forward = ldm_original_forward
elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
sd_unet.original_forward = sgm_original_forward
else:
sd_unet.original_forward = None
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
def undo_hijack(self, m):
conditioner = getattr(m, 'conditioner', None)
@ -291,7 +297,8 @@ class StableDiffusionModelHijack:
self.layers = None
self.clip = None
ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
sd_unet.original_forward = None
def apply_circular(self, enable):
if self.circular_enabled == enable:

View File

@ -1,22 +1,22 @@
import collections
import os.path
import sys
import gc
import threading
import torch
import re
import safetensors.torch
from omegaconf import OmegaConf
from omegaconf import OmegaConf, ListConfig
from os import mkdir
from urllib import request
import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
import tomesd
import numpy as np
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@ -49,11 +49,12 @@ class CheckpointInfo:
def __init__(self, filename):
self.filename = filename
abspath = os.path.abspath(filename)
abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
name = abspath.replace(abs_ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
@ -129,9 +130,12 @@ except Exception:
def setup_model():
"""called once at startup to do various one-time tasks related to SD models"""
os.makedirs(model_path, exist_ok=True)
enable_midas_autodownload()
patch_given_betas()
def checkpoint_tiles(use_short=False):
@ -309,6 +313,8 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
if checkpoint_info in checkpoints_loaded:
# use checkpoint cache
print(f"Loading weights [{sd_model_hash}] from cache")
# move to end as latest
checkpoints_loaded.move_to_end(checkpoint_info)
return checkpoints_loaded[checkpoint_info]
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@ -353,12 +359,12 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
if model.is_ssd:
sd_hijack.model_hijack.conv_ssd(model)
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
if shared.opts.sd_checkpoint_cache > 0:
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = state_dict
checkpoints_loaded[checkpoint_info] = state_dict.copy()
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
del state_dict
@ -456,6 +462,20 @@ def enable_midas_autodownload():
midas.api.load_model = load_model_wrapper
def patch_given_betas():
import ldm.models.diffusion.ddpm
def patched_register_schedule(*args, **kwargs):
"""a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
if isinstance(args[1], ListConfig):
args = (args[0], np.array(args[1]), *args[2:])
original_register_schedule(*args, **kwargs)
original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
def repair_config(sd_config):
if not hasattr(sd_config.model.params, "use_ema"):
@ -780,17 +800,7 @@ def reload_model_weights(sd_model=None, info=None):
def unload_model_weights(sd_model=None, info=None):
timer = Timer()
if model_data.sd_model:
model_data.sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
model_data.sd_model = None
sd_model = None
gc.collect()
devices.torch_gc()
print(f"Unloaded weights {timer.summary()}.")
send_model_to_cpu(sd_model or shared.sd_model)
return sd_model

View File

@ -21,7 +21,7 @@ config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inf
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
def is_using_v_parameterization_for_sd2(state_dict):
"""
@ -95,7 +95,10 @@ def guess_model_config_from_state_dict(sd, filename):
if diffusion_model_input.shape[1] == 8:
return config_instruct_pix2pix
if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
return config_alt_diffusion_m18
return config_alt_diffusion
return config_default

View File

@ -1,11 +1,11 @@
import torch.nn
import ldm.modules.diffusionmodules.openaimodel
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
original_forward = None
def list_unets():
@ -88,5 +88,5 @@ def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
return original_forward(self, x, timesteps, context, *args, **kwargs)

View File

@ -14,5 +14,5 @@ if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
else:
cmd_opts, _ = parser.parse_known_args()
cmd_opts.disable_extension_access = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name]) and not cmd_opts.enable_insecure_extension_access
cmd_opts.webui_is_non_local = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name])
cmd_opts.disable_extension_access = cmd_opts.webui_is_non_local and not cmd_opts.enable_insecure_extension_access

View File

@ -44,9 +44,9 @@ def refresh_unet_list():
modules.sd_unet.list_unets()
def list_checkpoint_tiles():
def list_checkpoint_tiles(use_short=False):
import modules.sd_models
return modules.sd_models.checkpoint_tiles()
return modules.sd_models.checkpoint_tiles(use_short)
def refresh_checkpoints():
@ -67,6 +67,8 @@ def reload_hypernetworks():
ui_reorder_categories_builtin_items = [
"prompt",
"image",
"inpaint",
"sampler",
"accordions",

View File

@ -26,7 +26,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"samples_format": OptionInfo('png', 'File format for images'),
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
"grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
@ -62,6 +62,8 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
"save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
"notification_audio": OptionInfo(True, "Play notification sound after image generation").info("notification.mp3 should be present in the root directory").needs_reload_ui(),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
@ -100,6 +102,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
options_templates.update(options_section(('system', "System"), {
"auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
"enable_console_prompts": OptionInfo(shared.cmd_opts.enable_console_prompts, "Print prompts to console when generating with txt2img and img2img."),
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
"show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
@ -109,6 +112,7 @@ options_templates.update(options_section(('system', "System"), {
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
"dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
}))
options_templates.update(options_section(('API', "API"), {
@ -133,7 +137,7 @@ options_templates.update(options_section(('training', "Training"), {
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles(shared.opts.sd_checkpoint_dropdown_use_short)}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
@ -230,6 +234,8 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
"extra_networks_card_order_field": OptionInfo("Name", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@ -255,15 +261,18 @@ options_templates.update(options_section(('ui', "User interface"), {
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
"keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Ctrl+up/down word delimiters"),
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"sd_checkpoint_dropdown_use_short": OptionInfo(False, "Checkpoint dropdown: use filenames without paths").info("models in subdirectories like photo/sd15.ckpt will be listed as just sd15.ckpt"),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
"compact_prompt_box": OptionInfo(False, "Compact prompt layout").info("puts prompt and negative prompt inside the Generate tab, leaving more vertical space for the image on the right").needs_reload_ui(),
}))
@ -305,8 +314,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule min sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule max sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
'rho': OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", infotext='Discard penultimate sigma').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
@ -329,4 +338,3 @@ options_templates.update(options_section((None, "Hidden options"), {
"restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))

View File

@ -103,6 +103,7 @@ class State:
def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.time_start = time.time()
self.job_count = -1
self.processing_has_refined_job_count = False
self.job_no = 0
@ -114,7 +115,6 @@ class State:
self.skipped = False
self.interrupted = False
self.textinfo = None
self.time_start = time.time()
self.job = job
devices.torch_gc()
log.info("Starting job %s", job)

View File

@ -15,7 +15,7 @@ import torch
from torch import Tensor
from torch.utils.checkpoint import checkpoint
import math
from typing import Optional, NamedTuple, List
from typing import Optional, NamedTuple
def narrow_trunc(
@ -97,7 +97,7 @@ def _query_chunk_attention(
)
return summarize_chunk(query, key_chunk, value_chunk)
chunks: List[AttnChunk] = [
chunks: list[AttnChunk] = [
chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
]
acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))

View File

@ -181,40 +181,7 @@ class EmbeddingDatabase:
else:
return
# textual inversion embeddings
if 'string_to_param' in data:
param_dict = data['string_to_param']
param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
vectors = data['clip_g'].shape[0]
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
embedding.vectors = vectors
embedding.shape = shape
embedding.filename = path
embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model)
@ -313,6 +280,45 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
return fn
def create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
if 'string_to_param' in data: # textual inversion embeddings
param_dict = data['string_to_param']
param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
emb = next(iter(param_dict.items()))[1]
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
vectors = data['clip_g'].shape[0]
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
emb = next(iter(data.values()))
if len(emb.shape) == 1:
emb = emb.unsqueeze(0)
vec = emb.detach().to(devices.device, dtype=torch.float32)
shape = vec.shape[-1]
vectors = vec.shape[0]
else:
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
embedding.vectors = vectors
embedding.shape = shape
if filepath:
embedding.filename = filepath
embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')
return embedding
def write_loss(log_directory, filename, step, epoch_len, values):
if shared.opts.training_write_csv_every == 0:
return
@ -386,7 +392,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
assert log_directory, "Log directory is empty"
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_name, preview_cfg_scale, preview_seed, preview_width, preview_height):
from modules import processing
save_embedding_every = save_embedding_every or 0
@ -590,7 +596,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

View File

@ -3,7 +3,7 @@ from contextlib import closing
import modules.scripts
from modules import processing
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.shared import opts, cmd_opts
from modules.shared import opts
import modules.shared as shared
from modules.ui import plaintext_to_html
import gradio as gr
@ -45,7 +45,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
p.user = request.username
if cmd_opts.enable_console_prompts:
if shared.opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
with closing(p):

View File

@ -12,7 +12,7 @@ from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
from modules.paths import script_path
from modules.ui_common import create_refresh_button
@ -25,7 +25,6 @@ import modules.hypernetworks.ui as hypernetworks_ui
import modules.textual_inversion.ui as textual_inversion_ui
import modules.textual_inversion.textual_inversion as textual_inversion
import modules.shared as shared
import modules.images
from modules import prompt_parser
from modules.sd_hijack import model_hijack
from modules.generation_parameters_copypaste import image_from_url_text
@ -151,11 +150,15 @@ def connect_clear_prompt(button):
)
def update_token_counter(text, steps):
def update_token_counter(text, steps, *, is_positive=True):
try:
text, _ = extra_networks.parse_prompt(text)
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
if is_positive:
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
else:
prompt_flat_list = [text]
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
except Exception:
@ -169,76 +172,9 @@ def update_token_counter(text, steps):
return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
class Toprow:
"""Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
def update_negative_prompt_token_counter(text, steps):
return update_token_counter(text, steps, is_positive=False)
def __init__(self, is_img2img):
id_part = "img2img" if is_img2img else "txt2img"
self.id_part = id_part
with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
with gr.Row():
with gr.Column(scale=80):
with gr.Row():
self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.button_interrogate = None
self.button_deepbooru = None
if is_img2img:
with gr.Column(scale=1, elem_classes="interrogate-col"):
self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
self.skip.click(
fn=lambda: shared.state.skip(),
inputs=[],
outputs=[],
)
self.interrupt.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
with gr.Row(elem_id=f"{id_part}_tools"):
self.paste = ToolButton(value=paste_symbol, elem_id="paste")
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
self.clear_prompt_button.click(
fn=lambda *x: x,
_js="confirm_clear_prompt",
inputs=[self.prompt, self.negative_prompt],
outputs=[self.prompt, self.negative_prompt],
)
self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
self.prompt_img.change(
fn=modules.images.image_data,
inputs=[self.prompt_img],
outputs=[self.prompt, self.prompt_img],
show_progress=False,
)
def setup_progressbar(*args, **kwargs):
@ -278,8 +214,8 @@ def apply_setting(key, value):
return getattr(opts, key)
def create_output_panel(tabname, outdir):
return ui_common.create_output_panel(tabname, outdir)
def create_output_panel(tabname, outdir, toprow=None):
return ui_common.create_output_panel(tabname, outdir, toprow)
def create_sampler_and_steps_selection(choices, tabname):
@ -326,7 +262,7 @@ def create_ui():
scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
toprow = Toprow(is_img2img=False)
toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
dummy_component = gr.Label(visible=False)
@ -338,6 +274,9 @@ def create_ui():
scripts.scripts_txt2img.prepare_ui()
for category in ordered_ui_categories():
if category == "prompt":
toprow.create_inline_toprow_prompts()
if category == "sampler":
steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
@ -348,7 +287,7 @@ def create_ui():
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"):
@ -432,7 +371,7 @@ def create_ui():
show_progress=False,
)
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
txt2img_args = dict(
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
@ -533,7 +472,7 @@ def create_ui():
]
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
@ -544,7 +483,7 @@ def create_ui():
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
with gr.Blocks(analytics_enabled=False) as img2img_interface:
toprow = Toprow(is_img2img=True)
toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
extra_tabs.__enter__()
@ -567,85 +506,89 @@ def create_ui():
button = gr.Button(title)
copy_image_buttons.append((button, name, elem))
with gr.Tabs(elem_id="mode_img2img"):
img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
def update_orig(image, state):
if image is not None:
same_size = state is not None and state.size == image.size
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
edited = same_size and has_exact_match
return image if not edited or state is None else state
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
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(
"<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." +
f"<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")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
for i, tab in enumerate(img2img_tabs):
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
def copy_image(img):
if isinstance(img, dict) and 'image' in img:
return img['image']
return img
for button, name, elem in copy_image_buttons:
button.click(
fn=copy_image,
inputs=[elem],
outputs=[copy_image_destinations[name]],
)
button.click(
fn=lambda: None,
_js=f"switch_to_{name.replace(' ', '_')}",
inputs=[],
outputs=[],
)
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
scripts.scripts_img2img.prepare_ui()
for category in ordered_ui_categories():
if category == "prompt":
toprow.create_inline_toprow_prompts()
if category == "image":
with gr.Tabs(elem_id="mode_img2img"):
img2img_selected_tab = gr.State(0)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
add_copy_image_controls('img2img', init_img)
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
add_copy_image_controls('sketch', sketch)
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
add_copy_image_controls('inpaint', init_img_with_mask)
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
inpaint_color_sketch_orig = gr.State(None)
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
def update_orig(image, state):
if image is not None:
same_size = state is not None and state.size == image.size
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
edited = same_size and has_exact_match
return image if not edited or state is None else state
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
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(
"<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." +
f"<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")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
for i, tab in enumerate(img2img_tabs):
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
def copy_image(img):
if isinstance(img, dict) and 'image' in img:
return img['image']
return img
for button, name, elem in copy_image_buttons:
button.click(
fn=copy_image,
inputs=[elem],
outputs=[copy_image_destinations[name]],
)
button.click(
fn=lambda: None,
_js=f"switch_to_{name.replace(' ', '_')}",
inputs=[],
outputs=[],
)
with FormRow():
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
if category == "sampler":
steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
@ -661,8 +604,8 @@ def create_ui():
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
@ -759,7 +702,7 @@ def create_ui():
if category not in {"accordions"}:
scripts.scripts_img2img.setup_ui_for_section(category)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
@ -1286,7 +1229,7 @@ def create_ui():
loadsave.setup_ui()
if os.path.exists(os.path.join(script_path, "notification.mp3")):
if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
footer = shared.html("footer.html")
@ -1338,7 +1281,6 @@ checkpoint: <a id="sd_checkpoint_hash">N/A</a>
def setup_ui_api(app):
from pydantic import BaseModel, Field
from typing import List
class QuicksettingsHint(BaseModel):
name: str = Field(title="Name of the quicksettings field")
@ -1347,7 +1289,7 @@ def setup_ui_api(app):
def quicksettings_hint():
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])

View File

@ -104,7 +104,7 @@ def save_files(js_data, images, do_make_zip, index):
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
def create_output_panel(tabname, outdir):
def create_output_panel(tabname, outdir, toprow=None):
def open_folder(f):
if not os.path.exists(f):
@ -130,12 +130,15 @@ Requested path was: {f}
else:
sp.Popen(["xdg-open", path])
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
with gr.Column(elem_id=f"{tabname}_results"):
if toprow:
toprow.create_inline_toprow_image()
generation_info = None
with gr.Column():
with gr.Column(variant='panel', elem_id=f"{tabname}_results_panel"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
generation_info = None
with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")

View File

@ -197,7 +197,7 @@ def update_config_states_table(state_name):
config_state = config_states.all_config_states[state_name]
config_name = config_state.get("name", "Config")
created_date = time.asctime(time.gmtime(config_state["created_at"]))
created_date = datetime.fromtimestamp(config_state["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
filepath = config_state.get("filepath", "<unknown>")
try:

View File

@ -1,3 +1,4 @@
import functools
import os.path
import urllib.parse
from pathlib import Path
@ -15,6 +16,17 @@ from modules.ui_components import ToolButton
extra_pages = []
allowed_dirs = set()
default_allowed_preview_extensions = ["png", "jpg", "jpeg", "webp", "gif"]
@functools.cache
def allowed_preview_extensions_with_extra(extra_extensions=None):
return set(default_allowed_preview_extensions) | set(extra_extensions or [])
def allowed_preview_extensions():
return allowed_preview_extensions_with_extra((shared.opts.samples_format, ))
def register_page(page):
"""registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
@ -33,9 +45,9 @@ def fetch_file(filename: str = ""):
if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
ext = os.path.splitext(filename)[1].lower()
if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
ext = os.path.splitext(filename)[1].lower()[1:]
if ext not in allowed_preview_extensions():
raise ValueError(f"File cannot be fetched: {filename}. Extensions allowed: {allowed_preview_extensions()}.")
# would profit from returning 304
return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
@ -91,6 +103,7 @@ class ExtraNetworksPage:
self.name = title.lower()
self.id_page = self.name.replace(" ", "_")
self.card_page = shared.html("extra-networks-card.html")
self.allow_prompt = True
self.allow_negative_prompt = False
self.metadata = {}
self.items = {}
@ -213,9 +226,9 @@ class ExtraNetworksPage:
metadata_button = ""
metadata = item.get("metadata")
if metadata:
metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(item['name'])})'></div>"
metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(html.escape(item['name']))})'></div>"
edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(item['name'])})'></div>"
edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(html.escape(item['name']))})'></div>"
local_path = ""
filename = item.get("filename", "")
@ -235,7 +248,7 @@ class ExtraNetworksPage:
if search_only and shared.opts.extra_networks_hidden_models == "Never":
return ""
sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
sort_keys = " ".join([f'data-sort-{k}="{html.escape(str(v))}"' for k, v in item.get("sort_keys", {}).items()]).strip()
args = {
"background_image": background_image,
@ -273,11 +286,7 @@ class ExtraNetworksPage:
Find a preview PNG for a given path (without extension) and call link_preview on it.
"""
preview_extensions = ["png", "jpg", "jpeg", "webp"]
if shared.opts.samples_format not in preview_extensions:
preview_extensions.append(shared.opts.samples_format)
potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in allowed_preview_extensions()], [])
for file in potential_files:
if os.path.isfile(file):
@ -359,7 +368,7 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs = []
for page in ui.stored_extra_pages:
with gr.Tab(page.title, id=page.id_page) as tab:
with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
elem_id = f"{tabname}_{page.id_page}_cards_html"
page_elem = gr.HTML('Loading...', elem_id=elem_id)
ui.pages.append(page_elem)
@ -373,19 +382,28 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
related_tabs.append(tab)
edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
dropdown_sort = gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes="sortorder", visible=False)
dropdown_sort = gr.Dropdown(choices=['Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
for tab in unrelated_tabs:
tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
tab_controls = [edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs]
for tab in related_tabs:
tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
for tab in unrelated_tabs:
tab.select(fn=lambda: [gr.update(visible=False) for _ in tab_controls], _js='function(){ extraNetworksUrelatedTabSelected("' + tabname + '"); }', inputs=[], outputs=tab_controls, show_progress=False)
for page, tab in zip(ui.stored_extra_pages, related_tabs):
allow_prompt = "true" if page.allow_prompt else "false"
allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls], _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)
dropdown_sort.change(fn=lambda: None, _js="function(){ applyExtraNetworkSort('" + tabname + "'); }")
def pages_html():
if not ui.pages_contents:

View File

@ -10,6 +10,8 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
def __init__(self):
super().__init__('Checkpoints')
self.allow_prompt = False
def refresh(self):
shared.refresh_checkpoints()

View File

@ -2,12 +2,12 @@ import os
import gradio as gr
from modules import localization, shared, scripts
from modules.paths import script_path, data_path
from modules.paths import script_path, data_path, cwd
def webpath(fn):
if fn.startswith(script_path):
web_path = os.path.relpath(fn, script_path).replace('\\', '/')
if fn.startswith(cwd):
web_path = os.path.relpath(fn, cwd)
else:
web_path = os.path.abspath(fn)

View File

@ -4,7 +4,7 @@ import os
import gradio as gr
from modules import errors
from modules.ui_components import ToolButton
from modules.ui_components import ToolButton, InputAccordion
def radio_choices(comp): # gradio 3.41 changes choices from list of values to list of pairs
@ -32,8 +32,6 @@ class UiLoadsave:
self.error_loading = True
errors.display(e, "loading settings")
def add_component(self, path, x):
"""adds component to the registry of tracked components"""
@ -43,20 +41,24 @@ class UiLoadsave:
key = f"{path}/{field}"
if getattr(obj, 'custom_script_source', None) is not None:
key = f"customscript/{obj.custom_script_source}/{key}"
key = f"customscript/{obj.custom_script_source}/{key}"
if getattr(obj, 'do_not_save_to_config', False):
return
saved_value = self.ui_settings.get(key, None)
if isinstance(obj, gr.Accordion) and isinstance(x, InputAccordion) and field == 'value':
field = 'open'
if saved_value is None:
self.ui_settings[key] = getattr(obj, field)
elif condition and not condition(saved_value):
pass
else:
if isinstance(x, gr.Textbox) and field == 'value': # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
if isinstance(obj, gr.Textbox) and field == 'value': # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
saved_value = str(saved_value)
elif isinstance(x, gr.Number) and field == 'value':
elif isinstance(obj, gr.Number) and field == 'value':
try:
saved_value = float(saved_value)
except ValueError:
@ -67,7 +69,7 @@ class UiLoadsave:
init_field(saved_value)
if field == 'value' and key not in self.component_mapping:
self.component_mapping[key] = x
self.component_mapping[key] = obj
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton, gr.Button] and x.visible:
apply_field(x, 'visible')
@ -100,6 +102,12 @@ class UiLoadsave:
apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
if type(x) == InputAccordion:
if x.accordion.visible:
apply_field(x.accordion, 'visible')
apply_field(x, 'value')
apply_field(x.accordion, 'value')
def check_tab_id(tab_id):
tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
if type(tab_id) == str:

View File

@ -4,6 +4,7 @@ from modules import shared, ui_common, ui_components, styles
styles_edit_symbol = '\U0001f58c\uFE0F' # 🖌️
styles_materialize_symbol = '\U0001f4cb' # 📋
styles_copy_symbol = '\U0001f4dd' # 📝
def select_style(name):
@ -52,6 +53,8 @@ def refresh_styles():
class UiPromptStyles:
def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
self.tabname = tabname
self.main_ui_prompt = main_ui_prompt
self.main_ui_negative_prompt = main_ui_negative_prompt
with gr.Row(elem_id=f"{tabname}_styles_row"):
self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
@ -61,7 +64,8 @@ class UiPromptStyles:
with gr.Row():
self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply_dialog", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
self.copy = ui_components.ToolButton(value=styles_copy_symbol, elem_id=f"{tabname}_style_copy", tooltip="Copy main UI prompt to style.")
with gr.Row():
self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
@ -96,15 +100,21 @@ class UiPromptStyles:
show_progress=False,
).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
self.materialize.click(
fn=materialize_styles,
inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
self.setup_apply_button(self.materialize)
self.copy.click(
fn=lambda p, n: (p, n),
inputs=[main_ui_prompt, main_ui_negative_prompt],
outputs=[self.prompt, self.neg_prompt],
show_progress=False,
).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
)
ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
def setup_apply_button(self, button):
button.click(
fn=materialize_styles,
inputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
outputs=[self.main_ui_prompt, self.main_ui_negative_prompt, self.dropdown],
show_progress=False,
).then(fn=None, _js="function(){update_"+self.tabname+"_tokens(); closePopup();}", show_progress=False)

View File

@ -1,10 +1,11 @@
import gradio as gr
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo, timer
from modules.call_queue import wrap_gradio_call
from modules.shared import opts
from modules.ui_components import FormRow
from modules.ui_gradio_extensions import reload_javascript
from concurrent.futures import ThreadPoolExecutor, as_completed
def get_value_for_setting(key):
@ -63,6 +64,9 @@ class UiSettings:
quicksettings_list = None
quicksettings_names = None
text_settings = None
show_all_pages = None
show_one_page = None
search_input = None
def run_settings(self, *args):
changed = []
@ -135,7 +139,7 @@ class UiSettings:
gr.Group()
current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
current_tab.__enter__()
current_row = gr.Column(variant='compact')
current_row = gr.Column(elem_id=f"column_settings_{elem_id}", variant='compact')
current_row.__enter__()
previous_section = item.section
@ -173,26 +177,43 @@ class UiSettings:
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
with gr.Row():
unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
unload_sd_model = gr.Button(value='Unload SD checkpoint to RAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Load SD checkpoint to VRAM from RAM', elem_id="sett_reload_sd_model")
with gr.Row():
calculate_all_checkpoint_hash = gr.Button(value='Calculate hash for all checkpoint', elem_id="calculate_all_checkpoint_hash")
calculate_all_checkpoint_hash_threads = gr.Number(value=1, label="Number of parallel calculations", elem_id="calculate_all_checkpoint_hash_threads", precision=0, minimum=1)
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
self.show_all_pages = gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
self.show_one_page = gr.Button(value="Show only one page", elem_id="settings_show_one_page", visible=False)
self.show_one_page.click(lambda: None)
self.search_input = gr.Textbox(value="", elem_id="settings_search", max_lines=1, placeholder="Search...", show_label=False)
self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
def call_func_and_return_text(func, text):
def handler():
t = timer.Timer()
func()
t.record(text)
return f'{text} in {t.total:.1f}s'
return handler
unload_sd_model.click(
fn=sd_models.unload_model_weights,
fn=call_func_and_return_text(sd_models.unload_model_weights, 'Unloaded the checkpoint'),
inputs=[],
outputs=[]
outputs=[self.result]
)
reload_sd_model.click(
fn=sd_models.reload_model_weights,
fn=call_func_and_return_text(lambda: sd_models.send_model_to_device(shared.sd_model), 'Loaded the checkpoint'),
inputs=[],
outputs=[]
outputs=[self.result]
)
request_notifications.click(
@ -241,6 +262,21 @@ class UiSettings:
outputs=[sysinfo_check_output],
)
def calculate_all_checkpoint_hash_fn(max_thread):
checkpoints_list = sd_models.checkpoints_list.values()
with ThreadPoolExecutor(max_workers=max_thread) as executor:
futures = [executor.submit(checkpoint.calculate_shorthash) for checkpoint in checkpoints_list]
completed = 0
for _ in as_completed(futures):
completed += 1
print(f"{completed} / {len(checkpoints_list)} ")
print("Finish calculating hash for all checkpoints")
calculate_all_checkpoint_hash.click(
fn=calculate_all_checkpoint_hash_fn,
inputs=[calculate_all_checkpoint_hash_threads],
)
self.interface = settings_interface
def add_quicksettings(self):
@ -294,3 +330,8 @@ class UiSettings:
outputs=[self.component_dict[k] for k in component_keys],
queue=False,
)
def search(self, text):
print(text)
return [gr.update(visible=text in (comp.label or "")) for comp in self.components]

141
modules/ui_toprow.py Normal file
View File

@ -0,0 +1,141 @@
import gradio as gr
from modules import shared, ui_prompt_styles
import modules.images
from modules.ui_components import ToolButton
class Toprow:
"""Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
prompt = None
prompt_img = None
negative_prompt = None
button_interrogate = None
button_deepbooru = None
interrupt = None
skip = None
submit = None
paste = None
clear_prompt_button = None
apply_styles = None
restore_progress_button = None
token_counter = None
token_button = None
negative_token_counter = None
negative_token_button = None
ui_styles = None
submit_box = None
def __init__(self, is_img2img, is_compact=False):
id_part = "img2img" if is_img2img else "txt2img"
self.id_part = id_part
self.is_img2img = is_img2img
self.is_compact = is_compact
if not is_compact:
with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
self.create_classic_toprow()
else:
self.create_submit_box()
def create_classic_toprow(self):
self.create_prompts()
with gr.Column(scale=1, elem_id=f"{self.id_part}_actions_column"):
self.create_submit_box()
self.create_tools_row()
self.create_styles_ui()
def create_inline_toprow_prompts(self):
if not self.is_compact:
return
self.create_prompts()
with gr.Row(elem_classes=["toprow-compact-stylerow"]):
with gr.Column(elem_classes=["toprow-compact-tools"]):
self.create_tools_row()
with gr.Column():
self.create_styles_ui()
def create_inline_toprow_image(self):
if not self.is_compact:
return
self.submit_box.render()
def create_prompts(self):
with gr.Column(elem_id=f"{self.id_part}_prompt_container", elem_classes=["prompt-container-compact"] if self.is_compact else [], scale=6):
with gr.Row(elem_id=f"{self.id_part}_prompt_row", elem_classes=["prompt-row"]):
self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img = gr.File(label="", elem_id=f"{self.id_part}_prompt_image", file_count="single", type="binary", visible=False)
with gr.Row(elem_id=f"{self.id_part}_neg_prompt_row", elem_classes=["prompt-row"]):
self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
self.prompt_img.change(
fn=modules.images.image_data,
inputs=[self.prompt_img],
outputs=[self.prompt, self.prompt_img],
show_progress=False,
)
def create_submit_box(self):
with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
self.submit_box = submit_box
self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
self.skip.click(
fn=lambda: shared.state.skip(),
inputs=[],
outputs=[],
)
self.interrupt.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
def create_tools_row(self):
with gr.Row(elem_id=f"{self.id_part}_tools"):
from modules.ui import paste_symbol, clear_prompt_symbol, restore_progress_symbol
self.paste = ToolButton(value=paste_symbol, elem_id="paste", tooltip="Read generation parameters from prompt or last generation if prompt is empty into user interface.")
self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{self.id_part}_clear_prompt", tooltip="Clear prompt")
self.apply_styles = ToolButton(value=ui_prompt_styles.styles_materialize_symbol, elem_id=f"{self.id_part}_style_apply", tooltip="Apply all selected styles to prompts.")
if self.is_img2img:
self.button_interrogate = ToolButton('📎', tooltip='Interrogate CLIP - use CLIP neural network to create a text describing the image, and put it into the prompt field', elem_id="interrogate")
self.button_deepbooru = ToolButton('📦', tooltip='Interrogate DeepBooru - use DeepBooru neural network to create a text describing the image, and put it into the prompt field', elem_id="deepbooru")
self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"])
self.token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_token_button")
self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"])
self.negative_token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_negative_token_button")
self.clear_prompt_button.click(
fn=lambda *x: x,
_js="confirm_clear_prompt",
inputs=[self.prompt, self.negative_prompt],
outputs=[self.prompt, self.negative_prompt],
)
def create_styles_ui(self):
self.ui_styles = ui_prompt_styles.UiPromptStyles(self.id_part, self.prompt, self.negative_prompt)
self.ui_styles.setup_apply_button(self.apply_styles)

164
modules/xlmr_m18.py Normal file
View File

@ -0,0 +1,164 @@
from transformers import BertPreTrainedModel,BertConfig
import torch.nn as nn
import torch
from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
from transformers import XLMRobertaModel,XLMRobertaTokenizer
from typing import Optional
class BertSeriesConfig(BertConfig):
def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
self.learn_encoder = learn_encoder
class BertSeriesModelWithTransformation(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
config_class = BertSeriesConfig
def __init__(self, config=None, **kargs):
# modify initialization for autoloading
if config is None:
config = XLMRobertaConfig()
config.attention_probs_dropout_prob= 0.1
config.bos_token_id=0
config.eos_token_id=2
config.hidden_act='gelu'
config.hidden_dropout_prob=0.1
config.hidden_size=1024
config.initializer_range=0.02
config.intermediate_size=4096
config.layer_norm_eps=1e-05
config.max_position_embeddings=514
config.num_attention_heads=16
config.num_hidden_layers=24
config.output_past=True
config.pad_token_id=1
config.position_embedding_type= "absolute"
config.type_vocab_size= 1
config.use_cache=True
config.vocab_size= 250002
config.project_dim = 1024
config.learn_encoder = False
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size,config.project_dim)
# self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
# self.pooler = lambda x: x[:,0]
# self.post_init()
self.has_pre_transformation = True
if self.has_pre_transformation:
self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_init()
def encode(self,c):
device = next(self.parameters()).device
text = self.tokenizer(c,
truncation=True,
max_length=77,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt")
text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
text["attention_mask"] = torch.tensor(
text['attention_mask']).to(device)
features = self(**text)
return features['projection_state']
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) :
r"""
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
# # last module outputs
# sequence_output = outputs[0]
# # project every module
# sequence_output_ln = self.pre_LN(sequence_output)
# # pooler
# pooler_output = self.pooler(sequence_output_ln)
# pooler_output = self.transformation(pooler_output)
# projection_state = self.transformation(outputs.last_hidden_state)
if self.has_pre_transformation:
sequence_output2 = outputs["hidden_states"][-2]
sequence_output2 = self.pre_LN(sequence_output2)
projection_state2 = self.transformation_pre(sequence_output2)
return {
"projection_state": projection_state2,
"last_hidden_state": outputs.last_hidden_state,
"hidden_states": outputs.hidden_states,
"attentions": outputs.attentions,
}
else:
projection_state = self.transformation(outputs.last_hidden_state)
return {
"projection_state": projection_state,
"last_hidden_state": outputs.last_hidden_state,
"hidden_states": outputs.hidden_states,
"attentions": outputs.attentions,
}
# return {
# 'pooler_output':pooler_output,
# 'last_hidden_state':outputs.last_hidden_state,
# 'hidden_states':outputs.hidden_states,
# 'attentions':outputs.attentions,
# 'projection_state':projection_state,
# 'sequence_out': sequence_output
# }
class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
base_model_prefix = 'roberta'
config_class= RobertaSeriesConfig

View File

@ -27,6 +27,6 @@ timm==0.9.2
tomesd==0.1.3
torch
torchdiffeq==0.2.3
torchsde==0.2.5
torchsde==0.2.6
transformers==4.30.2
httpx==0.24.1

View File

@ -124,16 +124,20 @@ document.addEventListener("DOMContentLoaded", function() {
* Add a ctrl+enter as a shortcut to start a generation
*/
document.addEventListener('keydown', function(e) {
var handled = false;
if (e.key !== undefined) {
if ((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
} else if (e.keyCode !== undefined) {
if ((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
}
if (handled) {
var button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
if (button) {
button.click();
const isEnter = e.key === 'Enter' || e.keyCode === 13;
const isModifierKey = e.metaKey || e.ctrlKey || e.altKey;
const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]');
const generateButton = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
if (isEnter && isModifierKey) {
if (interruptButton.style.display === 'block') {
interruptButton.click();
setTimeout(function() {
generateButton.click();
}, 500);
} else {
generateButton.click();
}
e.preventDefault();
}

View File

@ -29,7 +29,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
with FormRow():

View File

@ -5,11 +5,17 @@ import shlex
import modules.scripts as scripts
import gradio as gr
from modules import sd_samplers, errors
from modules import sd_samplers, errors, sd_models
from modules.processing import Processed, process_images
from modules.shared import state
def process_model_tag(tag):
info = sd_models.get_closet_checkpoint_match(tag)
assert info is not None, f'Unknown checkpoint: {tag}'
return info.name
def process_string_tag(tag):
return tag
@ -27,7 +33,7 @@ def process_boolean_tag(tag):
prompt_tags = {
"sd_model": None,
"sd_model": process_model_tag,
"outpath_samples": process_string_tag,
"outpath_grids": process_string_tag,
"prompt_for_display": process_string_tag,
@ -108,6 +114,7 @@ class Script(scripts.Script):
def ui(self, is_img2img):
checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
prompt_position = gr.Radio(["start", "end"], label="Insert prompts at the", elem_id=self.elem_id("prompt_position"), value="start")
prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
@ -118,9 +125,9 @@ class Script(scripts.Script):
# We don't shrink back to 1, because that causes the control to ignore [enter], and it may
# be unclear to the user that shift-enter is needed.
prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt], show_progress=False)
return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
return [checkbox_iterate, checkbox_iterate_batch, prompt_position, prompt_txt]
def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_position, prompt_txt: str):
lines = [x for x in (x.strip() for x in prompt_txt.splitlines()) if x]
p.do_not_save_grid = True
@ -156,7 +163,22 @@ class Script(scripts.Script):
copy_p = copy.copy(p)
for k, v in args.items():
setattr(copy_p, k, v)
if k == "sd_model":
copy_p.override_settings['sd_model_checkpoint'] = v
else:
setattr(copy_p, k, v)
if args.get("prompt") and p.prompt:
if prompt_position == "start":
copy_p.prompt = args.get("prompt") + " " + p.prompt
else:
copy_p.prompt = p.prompt + " " + args.get("prompt")
if args.get("negative_prompt") and p.negative_prompt:
if prompt_position == "start":
copy_p.negative_prompt = args.get("negative_prompt") + " " + p.negative_prompt
else:
copy_p.negative_prompt = p.negative_prompt + " " + args.get("negative_prompt")
proc = process_images(copy_p)
images += proc.images

View File

@ -205,13 +205,14 @@ def csv_string_to_list_strip(data_str):
class AxisOption:
def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None, prepare=None):
self.label = label
self.type = type
self.apply = apply
self.format_value = format_value
self.confirm = confirm
self.cost = cost
self.prepare = prepare
self.choices = choices
@ -536,6 +537,8 @@ class Script(scripts.Script):
if opt.choices is not None and not csv_mode:
valslist = vals_dropdown
elif opt.prepare is not None:
valslist = opt.prepare(vals)
else:
valslist = csv_string_to_list_strip(vals)
@ -773,6 +776,8 @@ class Script(scripts.Script):
# TODO: See previous comment about intentional data misalignment.
adj_g = g-1 if g > 0 else g
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
if not include_sub_grids: # if not include_sub_grids then skip saving after the first grid
break
if not include_sub_grids:
# Done with sub-grids, drop all related information:

View File

@ -83,8 +83,10 @@ div.compact{
white-space: nowrap;
}
.gradio-dropdown ul.options li.item {
padding: 0.05em 0;
@media (pointer:fine) {
.gradio-dropdown ul.options li.item {
padding: 0.05em 0;
}
}
.gradio-dropdown ul.options li.item.selected {
@ -202,6 +204,11 @@ div.block.gradio-accordion {
padding: 8px 8px;
}
input[type="checkbox"].input-accordion-checkbox{
vertical-align: sub;
margin-right: 0.5em;
}
/* txt2img/img2img specific */
@ -289,6 +296,13 @@ div.block.gradio-accordion {
min-height: 4.5em;
}
#txt2img_generate, #img2img_generate {
min-height: 4.5em;
}
.generate-box-compact #txt2img_generate, .generate-box-compact #img2img_generate {
min-height: 3em;
}
@media screen and (min-width: 2500px) {
#txt2img_gallery, #img2img_gallery {
min-height: 768px;
@ -396,6 +410,15 @@ div#extras_scale_to_tab div.form{
min-width: 0.5em;
}
div.toprow-compact-stylerow{
margin: 0.5em 0;
}
div.toprow-compact-tools{
min-width: fit-content !important;
max-width: fit-content;
}
/* settings */
#quicksettings {
align-items: end;
@ -421,6 +444,7 @@ div#extras_scale_to_tab div.form{
#settings > div{
border: none;
margin-left: 10em;
padding: 0 var(--spacing-xl);
}
#settings > div.tab-nav{
@ -435,6 +459,7 @@ div#extras_scale_to_tab div.form{
border: none;
text-align: left;
white-space: initial;
padding: 4px;
}
#settings_result{
@ -516,7 +541,8 @@ table.popup-table .link{
height: 20px;
background: #b4c0cc;
border-radius: 3px !important;
top: -20px;
top: -14px;
left: 0px;
width: 100%;
}
@ -581,7 +607,6 @@ table.popup-table .link{
width: 100%;
height: 100%;
overflow: auto;
background-color: rgba(20, 20, 20, 0.95);
}
.global-popup *{
@ -590,9 +615,6 @@ table.popup-table .link{
.global-popup-close:before {
content: "×";
}
.global-popup-close{
position: fixed;
right: 0.25em;
top: 0;
@ -601,10 +623,20 @@ table.popup-table .link{
font-size: 32pt;
}
.global-popup-close{
position: fixed;
left: 0;
top: 0;
width: 100%;
height: 100%;
background-color: rgba(20, 20, 20, 0.95);
}
.global-popup-inner{
display: inline-block;
margin: auto;
padding: 2em;
z-index: 1001;
}
/* fullpage image viewer */
@ -808,6 +840,10 @@ footer {
/* extra networks UI */
.extra-page .prompt{
margin: 0 0 0.5em 0;
}
.extra-network-cards{
height: calc(100vh - 24rem);
overflow: clip scroll;

View File

@ -1,6 +1,11 @@
@echo off
if exist webui.settings.bat (
call webui.settings.bat
)
if not defined PYTHON (set PYTHON=python)
if defined GIT (set "GIT_PYTHON_GIT_EXECUTABLE=%GIT%")
if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
set SD_WEBUI_RESTART=tmp/restart

View File

@ -74,7 +74,7 @@ def webui():
if shared.opts.auto_launch_browser == "Remote" or cmd_opts.autolaunch:
auto_launch_browser = True
elif shared.opts.auto_launch_browser == "Local":
auto_launch_browser = not any([cmd_opts.listen, cmd_opts.share, cmd_opts.ngrok, cmd_opts.server_name])
auto_launch_browser = not cmd_opts.webui_is_non_local
app, local_url, share_url = shared.demo.launch(
share=cmd_opts.share,

View File

@ -4,12 +4,6 @@
# change the variables in webui-user.sh instead #
#################################################
use_venv=1
if [[ $venv_dir == "-" ]]; then
use_venv=0
fi
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
@ -28,6 +22,12 @@ then
source "$SCRIPT_DIR"/webui-user.sh
fi
# If $venv_dir is "-", then disable venv support
use_venv=1
if [[ $venv_dir == "-" ]]; then
use_venv=0
fi
# Set defaults
# Install directory without trailing slash
if [[ -z "${install_dir}" ]]
@ -51,6 +51,8 @@ fi
if [[ -z "${GIT}" ]]
then
export GIT="git"
else
export GIT_PYTHON_GIT_EXECUTABLE="${GIT}"
fi
# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
@ -141,9 +143,8 @@ case "$gpu_info" in
*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
;;
*"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6"
# Navi 3 needs at least 5.5 which is only on the nightly chain, previous versions are no longer online (torch==2.1.0.dev-20230614+rocm5.5 torchvision==0.16.0.dev-20230614+rocm5.5 torchaudio==2.1.0.dev-20230614+rocm5.5)
# so switch to nightly rocm5.6 without explicit versions this time
export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/test/rocm5.6"
# Navi 3 needs at least 5.5 which is only on the torch 2.1.0 release candidates right now
;;
*"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
printf "\n%s\n" "${delimiter}"