Compare commits
13 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 60efbc4618 | |||
| a56190cdb1 | |||
| 04903af798 | |||
| e8c3b1f2a0 | |||
| 8bf30e3c42 | |||
| fbc51fa210 | |||
| 7025a2c4a5 | |||
| 0120768f63 | |||
| b425b97ad6 | |||
| 539ea3982d | |||
| 65bd61e87c | |||
| 95686227bd | |||
| df74c3c638 |
@@ -1,36 +1,69 @@
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// Stable Diffusion WebUI - Bracket checker
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// By Hingashi no Florin/Bwin4L & @akx
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// Stable Diffusion WebUI - Bracket Checker
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// By @Bwin4L, @akx, @w-e-w, @Haoming02
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// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
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// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
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// If there's a mismatch, the keyword counter turns red, and if you hover on it, a tooltip tells you what's wrong.
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function checkBrackets(textArea, counterElem) {
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const pairs = [
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['(', ')', 'round brackets'],
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['[', ']', 'square brackets'],
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['{', '}', 'curly brackets']
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];
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function checkBrackets(textArea, counterElt) {
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const counts = {};
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textArea.value.matchAll(/(?<!\\)(?:\\\\)*?([(){}[\]])/g).forEach(bracket => {
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counts[bracket[1]] = (counts[bracket[1]] || 0) + 1;
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});
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const errors = [];
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const errors = new Set();
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let i = 0;
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function checkPair(open, close, kind) {
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if (counts[open] !== counts[close]) {
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errors.push(
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`${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
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);
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while (i < textArea.value.length) {
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let char = textArea.value[i];
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let escaped = false;
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while (char === '\\' && i + 1 < textArea.value.length) {
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escaped = !escaped;
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i++;
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char = textArea.value[i];
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}
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if (escaped) {
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i++;
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continue;
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}
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for (const [open, close, label] of pairs) {
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if (char === open) {
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counts[label] = (counts[label] || 0) + 1;
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} else if (char === close) {
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counts[label] = (counts[label] || 0) - 1;
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if (counts[label] < 0) {
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errors.add(`Incorrect order of ${label}.`);
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}
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}
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}
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i++;
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}
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for (const [open, close, label] of pairs) {
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if (counts[label] == undefined) {
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continue;
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}
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if (counts[label] > 0) {
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errors.add(`${open} ... ${close} - Detected ${counts[label]} more opening than closing ${label}.`);
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} else if (counts[label] < 0) {
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errors.add(`${open} ... ${close} - Detected ${-counts[label]} more closing than opening ${label}.`);
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}
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}
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checkPair('(', ')', 'round brackets');
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checkPair('[', ']', 'square brackets');
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checkPair('{', '}', 'curly brackets');
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counterElt.title = errors.join('\n');
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counterElt.classList.toggle('error', errors.length !== 0);
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counterElem.title = [...errors].join('\n');
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counterElem.classList.toggle('error', errors.size !== 0);
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}
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function setupBracketChecking(id_prompt, id_counter) {
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var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
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var counter = gradioApp().getElementById(id_counter);
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const textarea = gradioApp().querySelector(`#${id_prompt} > label > textarea`);
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const counter = gradioApp().getElementById(id_counter);
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if (textarea && counter) {
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textarea.addEventListener("input", () => checkBrackets(textarea, counter));
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onEdit(`${id_prompt}_BracketChecking`, textarea, 400, () => checkBrackets(textarea, counter));
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}
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}
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@@ -249,6 +249,8 @@ class Api:
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self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-reload-ui", self.reload_webui, methods=["POST"])
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self.add_api_route("/sdapi/v1/server-reload-script-bodies", self.reload_script_bodies, methods=["POST"])
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self.default_script_arg_txt2img = []
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self.default_script_arg_img2img = []
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@@ -926,3 +928,10 @@ class Api:
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shared.state.server_command = "stop"
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return Response("Stopping.")
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def reload_webui(self):
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shared.state.request_restart()
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return Response("Reloading.")
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def reload_script_bodies(self):
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scripts.reload_script_body_only()
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return Response("Reload script bodies.")
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+2
-25
@@ -187,7 +187,6 @@ class StableDiffusionProcessing:
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cached_uc = [None, None]
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cached_c = [None, None]
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hijack_generation_params_state_list = []
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comments: dict = None
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sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
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@@ -481,10 +480,6 @@ class StableDiffusionProcessing:
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for cache in caches:
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if cache[0] is not None and cached_params == cache[0]:
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if len(cache) == 3:
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generation_params_state, cached_params_2 = cache[2]
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if cached_params == cached_params_2:
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self.hijack_generation_params_state_list.extend(generation_params_state)
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return cache[1]
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cache = caches[0]
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@@ -492,25 +487,9 @@ class StableDiffusionProcessing:
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with devices.autocast():
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cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
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generation_params_state = model_hijack.capture_generation_params_state()
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self.hijack_generation_params_state_list.extend(generation_params_state)
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if len(cache) == 2:
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cache.append((generation_params_state, cached_params))
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else:
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cache[2] = (generation_params_state, cached_params)
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cache[0] = cached_params
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return cache[1]
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def apply_hijack_generation_params(self):
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self.extra_generation_params.update(model_hijack.extra_generation_params)
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for func in self.hijack_generation_params_state_list:
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try:
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func(self.extra_generation_params)
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except Exception:
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errors.report('Failed to apply hijack generation params state', exc_info=True)
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self.hijack_generation_params_state_list.clear()
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def setup_conds(self):
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prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
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negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
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@@ -523,8 +502,6 @@ class StableDiffusionProcessing:
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self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
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self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
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self.apply_hijack_generation_params()
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def get_conds(self):
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return self.c, self.uc
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@@ -988,6 +965,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p.setup_conds()
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p.extra_generation_params.update(model_hijack.extra_generation_params)
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# params.txt should be saved after scripts.process_batch, since the
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# infotext could be modified by that callback
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# Example: a wildcard processed by process_batch sets an extra model
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@@ -1534,8 +1513,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
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self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)
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self.apply_hijack_generation_params()
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def setup_conds(self):
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if self.is_hr_pass:
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# if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
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@@ -6,7 +6,6 @@ from modules import devices, sd_hijack_optimizations, shared, script_callbacks,
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from modules.hypernetworks import hypernetwork
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from modules.shared import cmd_opts
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from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
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from modules.util import GenerationParamsState
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import ldm.modules.attention
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import ldm.modules.diffusionmodules.model
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@@ -322,13 +321,6 @@ class StableDiffusionModelHijack:
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self.comments = []
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self.extra_generation_params = {}
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def capture_generation_params_state(self):
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state = []
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for key in list(self.extra_generation_params):
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if isinstance(self.extra_generation_params[key], GenerationParamsState):
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state.append(self.extra_generation_params.pop(key))
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return state
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def get_prompt_lengths(self, text):
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if self.clip is None:
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return "-", "-"
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@@ -3,9 +3,8 @@ from collections import namedtuple
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import torch
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from modules import prompt_parser, devices, sd_hijack, sd_emphasis, util
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from modules import prompt_parser, devices, sd_hijack, sd_emphasis
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from modules.shared import opts
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from modules.util import GenerationParamsState
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class PromptChunk:
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@@ -28,31 +27,6 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
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are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
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class EmbeddingHashes(GenerationParamsState):
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def __init__(self, hashes: list):
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super().__init__()
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self.hashes = hashes
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def __call__(self, extra_generation_params):
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unique_hashes = dict.fromkeys(self.hashes)
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if existing_ti_hashes := extra_generation_params.get('TI hashes'):
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unique_hashes.update(dict.fromkeys(existing_ti_hashes.split(', ')))
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extra_generation_params['TI hashes'] = ', '.join(sorted(unique_hashes, key=util.natural_sort_key))
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class EmphasisMode(GenerationParamsState):
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def __init__(self, texts):
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super().__init__()
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if opts.emphasis != 'Original' and any(x for x in texts if '(' in x or '[' in x):
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self.emphasis = opts.emphasis
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else:
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self.emphasis = None
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def __call__(self, extra_generation_params):
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if self.emphasis:
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extra_generation_params['Emphasis'] = self.emphasis
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class TextConditionalModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@@ -264,9 +238,12 @@ class TextConditionalModel(torch.nn.Module):
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hashes.append(f"{name}: {shorthash}")
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if hashes:
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self.hijack.extra_generation_params["TI hashes"] = EmbeddingHashes(hashes)
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if self.hijack.extra_generation_params.get("TI hashes"):
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hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
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self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
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self.hijack.extra_generation_params["Emphasis"] = EmphasisMode(texts)
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if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
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self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
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if self.return_pooled:
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return torch.hstack(zs), zs[0].pooled
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@@ -33,12 +33,12 @@ categories.register_category("training", "Training")
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options_templates.update(options_section(('saving-images', "Saving images/grids", "saving"), {
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"samples_save": OptionInfo(True, "Always save all generated images"),
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"samples_format": OptionInfo('png', 'File format for images'),
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"samples_format": OptionInfo('png', 'File format for images', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
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"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"),
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"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
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"save_images_replace_action": OptionInfo("Replace", "Saving the image to an existing file", gr.Radio, {"choices": ["Replace", "Add number suffix"], **hide_dirs}),
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"grid_save": OptionInfo(True, "Always save all generated image grids"),
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"grid_format": OptionInfo('png', 'File format for grids'),
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"grid_format": OptionInfo('png', 'File format for grids', ui_components.DropdownEditable, {"choices": ("png", "jpg", "jpeg", "webp", "avif")}).info("manual input of <a href='https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html' target='_blank'>other formats</a> is possible, but compatibility is not guaranteed"),
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"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
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"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
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"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
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@@ -128,6 +128,7 @@ options_templates.update(options_section(('system', "System", "system"), {
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"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
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"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
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"dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
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"concurrent_git_fetch_limit": OptionInfo(16, "Number of simultaneous extension update checks ", gr.Slider, {"step": 1, "minimum": 1, "maximum": 100}).info("reduce extension update check time"),
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}))
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options_templates.update(options_section(('profiler', "Profiler", "system"), {
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@@ -1,5 +1,6 @@
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import json
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import os
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from concurrent.futures import ThreadPoolExecutor
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import threading
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import time
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from datetime import datetime, timezone
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@@ -106,18 +107,24 @@ def check_updates(id_task, disable_list):
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exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
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shared.state.job_count = len(exts)
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for ext in exts:
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shared.state.textinfo = ext.name
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lock = threading.Lock()
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def _check_update(ext):
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try:
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ext.check_updates()
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except FileNotFoundError as e:
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if 'FETCH_HEAD' not in str(e):
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raise
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except Exception:
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errors.report(f"Error checking updates for {ext.name}", exc_info=True)
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with lock:
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errors.report(f"Error checking updates for {ext.name}", exc_info=True)
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with lock:
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shared.state.textinfo = ext.name
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shared.state.nextjob()
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shared.state.nextjob()
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with ThreadPoolExecutor(max_workers=max(1, int(shared.opts.concurrent_git_fetch_limit))) as executor:
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for ext in exts:
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executor.submit(_check_update, ext)
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return extension_table(), ""
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@@ -288,18 +288,3 @@ def compare_sha256(file_path: str, hash_prefix: str) -> bool:
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for chunk in iter(lambda: f.read(blksize), b""):
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hash_sha256.update(chunk)
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return hash_sha256.hexdigest().startswith(hash_prefix.strip().lower())
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class GenerationParamsState:
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"""A custom class used in StableDiffusionModelHijack for assigning extra_generation_params
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generation_params assigned using this class will work properly with StableDiffusionProcessing.get_conds_with_caching()
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if assigned directly the generation_params will not be populated if conda cache is used
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Generation_params of this class will be captured (see StableDiffusionModelHijack.capture_generation_params_state) and stored with conda cache, and will be extracted in StableDiffusionProcessing.apply_hijack_generation_params()
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To use this class, create a subclass with a __call__ method that takes extra_generation_params: dict as input
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Example usage: sd_hijack_clip.EmbeddingHashes, sd_hijack_clip.EmphasisMode
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"""
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def __call__(self, extra_generation_params: dict):
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raise NotImplementedError
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|
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Reference in New Issue
Block a user