mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
Merge branch 'vladmandic:master' into master
This commit is contained in:
SourceHeartLock
GitHub
@@ -1,7 +1,7 @@
|
||||
name: Issue Report
|
||||
description: Something is broken
|
||||
title: "[Issue]: "
|
||||
labels: ["issue"]
|
||||
labels: []
|
||||
|
||||
body:
|
||||
- type: textarea
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
blank_issues_enabled: false
|
||||
contact_links:
|
||||
- name: WebUI Community Support
|
||||
- name: SD.Next Community Support
|
||||
url: https://github.com/vladmandic/automatic/discussions
|
||||
about: Please ask and answer questions here.
|
||||
|
||||
@@ -0,0 +1,33 @@
|
||||
name: Extension Compatibility Report
|
||||
description: Extension is not working as expected
|
||||
title: "[Extension]: "
|
||||
labels: ["extension"]
|
||||
|
||||
body:
|
||||
- type: textarea
|
||||
id: description
|
||||
attributes:
|
||||
label: Issue Description
|
||||
description: Tell us what happened in a very clear and simple way
|
||||
value: Please fill this form with as much information as possible
|
||||
- type: textarea
|
||||
id: platform
|
||||
attributes:
|
||||
label: Version Platform Description
|
||||
description: Describe your platform (program version, OS, browser)
|
||||
value:
|
||||
- type: textarea
|
||||
id: url
|
||||
attributes:
|
||||
label: URL link of the extension
|
||||
description: URL link of the extension
|
||||
value:
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
Any issues without version information and actual link to extension will be closed
|
||||
- type: markdown
|
||||
attributes:
|
||||
value: |
|
||||
If issue is extension installation or startup related, please check `setup.log` before reporting
|
||||
And when posting console logs, please use code blocks ( \`\`\` ) to format them insead of uploading screenshots
|
||||
Submodule extensions-builtin/a1111-sd-webui-lycoris updated: 514511d726...1f3e452c31
Submodule extensions-builtin/sd-dynamic-thresholding updated: 5d3465c4b2...c9721ab01a
Submodule extensions-builtin/sd-extension-system-info updated: 70ab5cf312...21d204a502
+1
-1
@@ -240,7 +240,7 @@ def check_torch():
|
||||
for i in range(0, torch_directml.device_count()):
|
||||
log.info(f'Torch detected GPU: {torch_directml.device_name(i)}')
|
||||
except:
|
||||
log.warning("Torch repoorts CUDA not available")
|
||||
log.warning("Torch reports CUDA not available")
|
||||
except Exception as e:
|
||||
log.error(f'Could not load torch: {e}')
|
||||
if not args.ignore:
|
||||
|
||||
@@ -15,6 +15,7 @@
|
||||
"camelcase":"off",
|
||||
"no-unused-vars":"off",
|
||||
"no-plusplus":"off",
|
||||
"no-param-reassign":"off"
|
||||
"no-param-reassign":"off",
|
||||
"no-restricted-syntax":"off"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -81,7 +81,7 @@ svg.feather.feather-image, .feather .feather-image { display: none }
|
||||
#quicksettings .gr-button-tool { font-size: 1.6rem; box-shadow: none; margin-left: -20px; margin-top: -2px; height: 2.4em; }
|
||||
#quicksettings > div, #quicksettings > fieldset { min-width: 26em; max-width: 26em; line-height: 2em; }
|
||||
#refresh_sd_model_checkpoint { height: 48px; margin-left: -14px; background: #333333; box-shadow: none; }
|
||||
#refresh_txt2img_styles, #refresh_img2img_styles, #open_folder_txt2img, #open_folder_img2img, #open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_res_switch_btn, #img2img_res_switch_btn, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h, #txt2img_tiling { display: none; }
|
||||
#refresh_txt2img_styles, #refresh_img2img_styles, #open_folder_txt2img, #open_folder_img2img, #open_folder_extras, #footer, #style_pos_col, #style_neg_col, #roll_col, #extras_upscaler_2, #extras_upscaler_2_visibility, #txt2img_res_switch_btn, #img2img_res_switch_btn, #txt2img_seed_resize_from_w, #txt2img_seed_resize_from_h { display: none; }
|
||||
#save-animation { border-radius: 0 !important; margin-bottom: 16px; background-color: #111111; }
|
||||
#script_list { padding: 4px; margin-top: 20px; margin-bottom: 20px; }
|
||||
#settings > div.flex-wrap { width: 15em; }
|
||||
|
||||
@@ -623,3 +623,5 @@ footer {
|
||||
.extra-network-cards .card ul a{ cursor: pointer; }
|
||||
.extra-network-cards .card ul a:hover{ color: red; }
|
||||
.theme-preview { display: none; position: fixed; border: 4px solid var(--neutral-600); box-shadow: 2px 2px 2px 2px var(--neutral-700); top: 0; bottom: 0; left: 0; right: 0; margin: auto; max-width: 75vw; z-index: 999; }
|
||||
|
||||
#scripts_alwayson_txt2img, scripts_alwayson_img2img { display: grid }
|
||||
|
||||
+31
-7
@@ -1,6 +1,7 @@
|
||||
/* global gradioApp */
|
||||
|
||||
let opts = {};
|
||||
window.opts = {};
|
||||
let tabSelected = '';
|
||||
|
||||
function set_theme(theme) {
|
||||
const gradioURL = window.location.href;
|
||||
@@ -192,6 +193,7 @@ function recalculate_prompts_inpaint(...args) {
|
||||
}
|
||||
|
||||
onUiUpdate(() => {
|
||||
sort_ui_elements();
|
||||
if (Object.keys(opts).length !== 0) return;
|
||||
const json_elem = gradioApp().getElementById('settings_json');
|
||||
if (!json_elem) return;
|
||||
@@ -226,7 +228,6 @@ onUiUpdate(() => {
|
||||
localTextarea.addEventListener('input', promptTokecountUpdateFuncs[id]);
|
||||
}
|
||||
|
||||
sort_ui_elements();
|
||||
registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
|
||||
registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
|
||||
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
|
||||
@@ -290,7 +291,7 @@ function monitor_server_status() {
|
||||
<h1>Waiting for server...</h1>
|
||||
<script>
|
||||
function monitor_server_status() {
|
||||
fetch('http://127.0.0.1:7860/sdapi/v1/progress')
|
||||
fetch('/sdapi/v1/progress')
|
||||
.then((res) => { !res?.ok ? setTimeout(monitor_server_status, 1000) : location.reload(); })
|
||||
.catch((e) => setTimeout(monitor_server_status, 1000))
|
||||
}
|
||||
@@ -305,7 +306,7 @@ function monitor_server_status() {
|
||||
function restart_reload() {
|
||||
document.body.style = 'background: #222222; font-size: 1rem; font-family:monospace; margin-top:20%; color:lightgray; text-align:center';
|
||||
document.body.innerHTML = '<h1>Server shutdown in progress...</h1>';
|
||||
fetch('http://127.0.0.1:7860/sdapi/v1/progress')
|
||||
fetch('/sdapi/v1/progress')
|
||||
.then((res) => setTimeout(restart_reload, 1000))
|
||||
.catch((e) => setTimeout(monitor_server_status, 500));
|
||||
return [];
|
||||
@@ -343,9 +344,32 @@ function create_theme_element() {
|
||||
}
|
||||
|
||||
function sort_ui_elements() {
|
||||
const tabs = gradioApp().getElementById('tabs');
|
||||
const scripts = gradioApp().getElementById('scripts_alwayson');
|
||||
console.log('HERE', opts, tabs, scripts);
|
||||
// sort top-level tabs
|
||||
const currSelected = gradioApp()?.querySelector('.tab-nav > .selected')?.innerText;
|
||||
if (currSelected === tabSelected || !opts.ui_tab_reorder) return;
|
||||
tabSelected = currSelected;
|
||||
const tabs = gradioApp().getElementById('tabs')?.children[0];
|
||||
if (!tabs) return;
|
||||
let tabsOrder = opts.ui_tab_reorder?.split(',').map((el) => el.trim().toLowerCase()) || [];
|
||||
for (const el of Array.from(tabs.children)) {
|
||||
const elIndex = tabsOrder.indexOf(el.innerText.toLowerCase());
|
||||
if (elIndex > -1) el.style.order = elIndex - 50; // default is 0 so setting to negative values
|
||||
}
|
||||
// sort always-on scripts
|
||||
const find = (el, ordered) => {
|
||||
for (const i in ordered) {
|
||||
if (el.innerText.toLowerCase().startsWith(ordered[i])) return i;
|
||||
}
|
||||
return 99;
|
||||
};
|
||||
|
||||
tabsOrder = opts.ui_scripts_reorder?.split(',').map((el) => el.trim().toLowerCase()) || [];
|
||||
|
||||
const scriptsTxt = gradioApp().getElementById('scripts_alwayson_txt2img').children;
|
||||
for (const el of Array.from(scriptsTxt)) el.style.order = find(el, tabsOrder);
|
||||
|
||||
const scriptsImg = gradioApp().getElementById('scripts_alwayson_img2img');
|
||||
for (const el of Array.from(scriptsImg)) el.style.order = find(el, tabsOrder);
|
||||
}
|
||||
|
||||
function preview_theme() {
|
||||
|
||||
@@ -15,18 +15,14 @@ from modules import shared, images, sd_models, sd_vae, sd_models_config
|
||||
def run_pnginfo(image):
|
||||
if image is None:
|
||||
return '', '', ''
|
||||
|
||||
geninfo, items = images.read_info_from_image(image)
|
||||
items = {**{'parameters': geninfo}, **items}
|
||||
|
||||
info = ''
|
||||
for key, text in items.items():
|
||||
info += f"<div><b>{html.escape(str(key))}</b>: {html.escape(str(text))}</div>"
|
||||
|
||||
if len(info) == 0:
|
||||
message = "Nothing found in the image."
|
||||
info = f"<div><p>{message}<p></div>"
|
||||
|
||||
return '', geninfo, info
|
||||
|
||||
|
||||
@@ -43,13 +39,10 @@ def create_config(ckpt_result, config_source, a, b, c):
|
||||
cfg = config(c)
|
||||
else:
|
||||
cfg = None
|
||||
|
||||
if cfg is None:
|
||||
return
|
||||
|
||||
filename, _ = os.path.splitext(ckpt_result)
|
||||
checkpoint_filename = filename + ".yaml"
|
||||
|
||||
shared.log.info("Copying config: {cfg} -> {checkpoint_filename}")
|
||||
shutil.copyfile(cfg, checkpoint_filename)
|
||||
|
||||
@@ -60,7 +53,6 @@ checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embedd
|
||||
def to_half(tensor, enable):
|
||||
if enable and tensor.dtype == torch.float:
|
||||
return tensor.half()
|
||||
|
||||
return tensor
|
||||
|
||||
|
||||
|
||||
@@ -11,6 +11,7 @@ from modules.memstats import memory_stats
|
||||
|
||||
|
||||
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
||||
shared.log.debug(f'batch: {input_dir}|{output_dir}|{inpaint_mask_dir}')
|
||||
processing.fix_seed(p)
|
||||
images = shared.listfiles(input_dir)
|
||||
is_inpaint_batch = False
|
||||
@@ -68,6 +69,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
|
||||
if shared.sd_model is None:
|
||||
shared.log.warning('Model not loaded')
|
||||
return
|
||||
shared.log.debug(f'img2img: {id_task}|{mode}|{prompt}|{negative_prompt}|{prompt_styles}|{init_img}|{sketch}|{init_img_with_mask}|{inpaint_color_sketch}|{inpaint_color_sketch_orig}|{init_img_inpaint}|{init_mask_inpaint}|{steps}|{sampler_index}|{mask_blur}|{mask_alpha}|{inpainting_fill}|{restore_faces}|{tiling}|{n_iter}|{batch_size}|{cfg_scale}|{image_cfg_scale}|{denoising_strength}|{seed}|{subseed}|{subseed_strength}|{seed_resize_from_h}|{seed_resize_from_w}|{seed_enable_extras}|{selected_scale_tab}|{height}|{width}|{scale_by}|{resize_mode}|{inpaint_full_res}|{inpaint_full_res_padding}|{inpainting_mask_invert}|{img2img_batch_input_dir}|{img2img_batch_output_dir}|{img2img_batch_inpaint_mask_dir}|{override_settings_texts}')
|
||||
|
||||
override_settings = create_override_settings_dict(override_settings_texts)
|
||||
|
||||
|
||||
+16
-51
@@ -449,7 +449,6 @@ def fix_seed(p):
|
||||
|
||||
def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): # pylint: disable=unused-argument
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
generation_params = {
|
||||
"Steps": p.steps,
|
||||
"Sampler": p.sampler_name,
|
||||
@@ -479,11 +478,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
|
||||
"Token merging stride y": None if opts.token_merging_stride_y == 2 else opts.token_merging_stride_y
|
||||
}
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
||||
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
|
||||
|
||||
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
|
||||
|
||||
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
||||
@@ -542,17 +538,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
assert len(p.prompt) > 0
|
||||
else:
|
||||
assert p.prompt is not None
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
seed = get_fixed_seed(p.seed)
|
||||
subseed = get_fixed_seed(p.subseed)
|
||||
|
||||
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
||||
modules.sd_hijack.model_hijack.clear_comments()
|
||||
|
||||
comments = {}
|
||||
|
||||
if type(p.prompt) == list:
|
||||
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
|
||||
else:
|
||||
@@ -562,12 +553,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
|
||||
else:
|
||||
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
|
||||
|
||||
if type(seed) == list:
|
||||
p.all_seeds = seed
|
||||
else:
|
||||
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
|
||||
|
||||
if type(subseed) == list:
|
||||
p.all_subseeds = subseed
|
||||
else:
|
||||
@@ -578,13 +567,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
if os.path.exists(opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
||||
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.process(p)
|
||||
|
||||
infotexts = []
|
||||
output_images = []
|
||||
|
||||
cached_uc = [None, None]
|
||||
cached_c = [None, None]
|
||||
|
||||
@@ -598,13 +584,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
have been used before. The second element is where the previously
|
||||
computed result is stored.
|
||||
"""
|
||||
|
||||
if cache[0] is not None and (required_prompts, steps) == cache[0]:
|
||||
return cache[1]
|
||||
|
||||
with devices.autocast():
|
||||
cache[1] = function(shared.sd_model, required_prompts, steps)
|
||||
|
||||
cache[0] = (required_prompts, steps)
|
||||
return cache[1]
|
||||
|
||||
@@ -613,49 +596,33 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
||||
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
|
||||
sd_vae_approx.model()
|
||||
|
||||
if state.job_count == -1:
|
||||
state.job_count = p.n_iter
|
||||
|
||||
extra_network_data = None
|
||||
for n in range(p.n_iter):
|
||||
p.iteration = n
|
||||
|
||||
if state.skipped:
|
||||
state.skipped = False
|
||||
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
|
||||
|
||||
if len(prompts) == 0:
|
||||
break
|
||||
|
||||
prompts, extra_network_data = extra_networks.parse_prompts(prompts)
|
||||
|
||||
if not p.disable_extra_networks:
|
||||
with devices.autocast():
|
||||
extra_networks.activate(p, extra_network_data)
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
|
||||
|
||||
# params.txt should be saved after scripts.process_batch, since the
|
||||
# infotext could be modified by that callback
|
||||
# Example: a wildcard processed by process_batch sets an extra model
|
||||
# strength, which is saved as "Model Strength: 1.0" in the infotext
|
||||
if n == 0:
|
||||
with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
file.write(processed.infotext(p, 0))
|
||||
|
||||
step_multiplier = 1
|
||||
if not shared.opts.dont_fix_second_order_samplers_schedule:
|
||||
try:
|
||||
@@ -664,17 +631,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
pass
|
||||
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
|
||||
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
for comment in model_hijack.comments:
|
||||
comments[comment] = 1
|
||||
|
||||
if p.n_iter > 1:
|
||||
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
|
||||
|
||||
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
|
||||
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
|
||||
|
||||
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
|
||||
try:
|
||||
for x in x_samples_ddim:
|
||||
@@ -690,45 +653,41 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
devices.test_for_nans(x, "vae")
|
||||
else:
|
||||
raise e
|
||||
|
||||
x_samples_ddim = torch.stack(x_samples_ddim).float()
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
del samples_ddim
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
|
||||
|
||||
for i, x_sample in enumerate(x_samples_ddim):
|
||||
p.batch_index = i
|
||||
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
|
||||
if p.restore_faces:
|
||||
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
|
||||
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
|
||||
|
||||
orig = p.restore_faces
|
||||
p.restore_faces = False
|
||||
info=infotext(n, i)
|
||||
p.restore_faces = orig
|
||||
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-face-restoration")
|
||||
devices.torch_gc()
|
||||
|
||||
x_sample = modules.face_restoration.restore_faces(x_sample)
|
||||
devices.torch_gc()
|
||||
|
||||
image = Image.fromarray(x_sample)
|
||||
|
||||
if p.scripts is not None:
|
||||
pp = scripts.PostprocessImageArgs(image)
|
||||
p.scripts.postprocess_image(p, pp)
|
||||
image = pp.image
|
||||
|
||||
if p.color_corrections is not None and i < len(p.color_corrections):
|
||||
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
|
||||
orig = p.color_corrections
|
||||
p.color_corrections = None
|
||||
info=infotext(n, i)
|
||||
p.color_corrections = orig
|
||||
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
|
||||
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
|
||||
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=info, p=p, suffix="-before-color-correction")
|
||||
image = apply_color_correction(p.color_corrections[i], image)
|
||||
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
|
||||
if opts.samples_save and not p.do_not_save_samples:
|
||||
@@ -878,7 +837,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
return
|
||||
if not isinstance(image, Image.Image):
|
||||
image = sd_samplers.sample_to_image(image, index, approximation=0)
|
||||
orig1 = self.extra_generation_params
|
||||
orig2 = self.restore_faces
|
||||
self.extra_generation_params = {}
|
||||
self.restore_faces = False
|
||||
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
|
||||
self.extra_generation_params = orig1
|
||||
self.restore_faces = orig2
|
||||
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
|
||||
|
||||
if latent_scale_mode is not None:
|
||||
|
||||
+3
-2
@@ -256,6 +256,7 @@ class ScriptRunner:
|
||||
self.infotext_fields = []
|
||||
self.paste_field_names = []
|
||||
self.script_load_ctr = 0
|
||||
self.is_img2img = False
|
||||
|
||||
def initialize_scripts(self, is_img2img):
|
||||
from modules import scripts_auto_postprocessing
|
||||
@@ -267,6 +268,7 @@ class ScriptRunner:
|
||||
self.infotext_fields.clear()
|
||||
self.paste_field_names.clear()
|
||||
self.script_load_ctr = 0
|
||||
self.is_img2img = is_img2img
|
||||
|
||||
self.scripts.clear()
|
||||
self.alwayson_scripts.clear()
|
||||
@@ -308,10 +310,9 @@ class ScriptRunner:
|
||||
inputs_alwayson += [script.alwayson for _ in controls]
|
||||
script.args_to = len(inputs)
|
||||
|
||||
with gr.Group(elem_id='scripts_alwayson'):
|
||||
with gr.Group(elem_id='scripts_alwayson_img2img' if self.is_img2img else 'scripts_alwayson_txt2img'):
|
||||
for script in self.alwayson_scripts:
|
||||
elem_id = f'script_{"txt2img" if script.is_txt2img else "img2img"}_{script.title().lower().replace(" ", "_")}'
|
||||
print(elem_id)
|
||||
with gr.Group(elem_id=elem_id) as group:
|
||||
create_script_ui(script, inputs, inputs_alwayson)
|
||||
script.group = group
|
||||
|
||||
@@ -149,4 +149,3 @@ class ScriptPostprocessingRunner:
|
||||
def image_changed(self):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
script.image_changed()
|
||||
|
||||
|
||||
@@ -103,7 +103,8 @@ def list_models():
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=None, command_path=shared.opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name=None, ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
|
||||
if shared.cmd_opts.ckpt is not None:
|
||||
if not os.path.exists(shared.cmd_opts.ckpt):
|
||||
shared.log.warning(f"Requested checkpoint not found: {shared.cmd_opts.ckpt}")
|
||||
if shared.cmd_opts.ckpt.lower() != "none":
|
||||
shared.log.warning(f"Requested checkpoint not found: {shared.cmd_opts.ckpt}")
|
||||
else:
|
||||
checkpoint_info = CheckpointInfo(shared.cmd_opts.ckpt)
|
||||
checkpoint_info.register()
|
||||
@@ -152,7 +153,7 @@ def model_hash(filename):
|
||||
def select_checkpoint():
|
||||
model_checkpoint = shared.opts.sd_model_checkpoint
|
||||
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
|
||||
if checkpoint_info is not None:
|
||||
if checkpoint_info is not None or shared.cmd_opts.ckpt is not None:
|
||||
return checkpoint_info
|
||||
if len(checkpoints_list) == 0:
|
||||
shared.log.error("Cannot run without a checkpoint")
|
||||
@@ -420,7 +421,7 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
shared.log.debug('Reload model weights skip')
|
||||
skip_next_load = False
|
||||
return
|
||||
shared.log.debug(f'Reload model weights: {sd_model} {info}')
|
||||
shared.log.debug(f'Reload model weights: {sd_model is not None} {info}')
|
||||
from modules import lowvram, sd_hijack
|
||||
checkpoint_info = info or select_checkpoint()
|
||||
if not sd_model:
|
||||
|
||||
@@ -32,7 +32,11 @@ def set_samplers():
|
||||
global samplers, samplers_for_img2img
|
||||
|
||||
shown_img2img = set(shared.opts.show_samplers)
|
||||
shown = set(shared.opts.show_samplers + ['PLMS', 'UniPC'])
|
||||
|
||||
if len(shared.opts.show_samplers) == 0:
|
||||
shown = {'PLMS', 'UniPC'}
|
||||
else:
|
||||
shown = set(shared.opts.show_samplers + ['PLMS'])
|
||||
|
||||
samplers = [x for x in all_samplers if x.name in shown]
|
||||
samplers_for_img2img = [x for x in all_samplers if x.name in shown_img2img]
|
||||
|
||||
+7
-5
@@ -242,7 +242,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"enable_quantization": OptionInfo(True, "Enable quantization in K samplers for sharper and cleaner results"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
|
||||
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to FP32"),
|
||||
"cross_attention_optimization": OptionInfo("Sub-quadratic" if is_device_dml else "Scaled-Dot-Product", "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
|
||||
"cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
|
||||
"sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size for the layer optimization to use", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
|
||||
@@ -285,9 +285,9 @@ options_templates.update(options_section(('saving-images', "Image options"), {
|
||||
"grid_prevent_empty_spots": OptionInfo(True, "Prevent empty spots in grid (when set to autodetect)"),
|
||||
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters"),
|
||||
"save_images_before_face_restoration": OptionInfo(True, "Save a copy of image before doing face restoration"),
|
||||
"save_images_before_highres_fix": OptionInfo(True, "Save a copy of image before applying highres fix"),
|
||||
"save_images_before_color_correction": OptionInfo(True, "Save a copy of image before applying color correction to img2img results"),
|
||||
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration"),
|
||||
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix"),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
|
||||
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
|
||||
"jpeg_quality": OptionInfo(85, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
|
||||
@@ -405,6 +405,8 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), # pylint: disable=anomalous-backslash-in-string
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
|
||||
"ui_tab_reorder": OptionInfo("From Text, From Image, Process Image", "UI tabs order"),
|
||||
"ui_scripts_reorder": OptionInfo("Enable Dynamic Thresholding, ControlNet", "UI scripts order"),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
}))
|
||||
@@ -422,7 +424,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ SDE", "DPM++ SDE", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
|
||||
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ SDE", "DPM++ SDE", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
|
||||
"fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
|
||||
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
|
||||
@@ -8,9 +8,12 @@ from modules.memstats import memory_stats
|
||||
|
||||
|
||||
def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args): # pylint: disable=unused-argument
|
||||
|
||||
if shared.sd_model is None:
|
||||
shared.log.warning('Model not loaded')
|
||||
return
|
||||
shared.log.debug(f'txt2img: {id_task}|{prompt}|{negative_prompt}|{prompt_styles}|{steps}|{sampler_index}|{restore_faces}|{tiling}|{n_iter}|{batch_size}|{cfg_scale}|{seed}|{subseed}|{subseed_strength}|{seed_resize_from_h}|{seed_resize_from_w}|{seed_enable_extras}|{height}|{width}|{enable_hr}|{denoising_strength}|{hr_scale}|{hr_upscaler}|{hr_second_pass_steps}|{hr_resize_x}|{hr_resize_y}|{override_settings_texts}')
|
||||
|
||||
override_settings = create_override_settings_dict(override_settings_texts)
|
||||
p = StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
|
||||
+11
-7
@@ -303,7 +303,14 @@ def create_output_panel(tabname, outdir):
|
||||
|
||||
def create_sampler_and_steps_selection(choices, tabname):
|
||||
with FormRow(elem_id=f"sampler_selection_{tabname}"):
|
||||
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value="UniPC" if tabname == 'txt2img' else "Euler a", type="index")
|
||||
if 'UniPC' in [sampler.name for sampler in choices]:
|
||||
chosen_sampler_name = 'UniPC'
|
||||
elif 'Euler a' in [sampler.name for sampler in choices]:
|
||||
chosen_sampler_name = 'Euler a'
|
||||
else:
|
||||
chosen_sampler_name = samplers[0].name
|
||||
|
||||
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=chosen_sampler_name if tabname == 'txt2img' else "Euler a", type="index")
|
||||
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
||||
return steps, sampler_index
|
||||
|
||||
@@ -890,7 +897,6 @@ def create_ui():
|
||||
with gr.Blocks(analytics_enabled=False) as train_interface:
|
||||
with gr.Column(elem_id='ti_train_container'):
|
||||
with gr.Tabs(elem_id="train_tabs"):
|
||||
|
||||
with gr.Tab(label="Merge models") as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='compact'):
|
||||
@@ -1406,15 +1412,13 @@ def create_ui():
|
||||
interfaces = [
|
||||
(txt2img_interface, "From Text", "txt2img"),
|
||||
(img2img_interface, "From Image", "img2img"),
|
||||
(extras_interface, "Process Image", "extras"),
|
||||
(extras_interface, "Process Image", "process"),
|
||||
# (pnginfo_interface, "Image Info", "pnginfo"),
|
||||
# (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
||||
(train_interface, "Train", "ti"),
|
||||
(train_interface, "Train", "train"),
|
||||
]
|
||||
|
||||
interfaces += script_callbacks.ui_tabs_callback()
|
||||
interfaces += [(settings_interface, "Settings", "settings")]
|
||||
|
||||
extensions_interface = ui_extensions.create_ui()
|
||||
interfaces += [(extensions_interface, "Extensions", "extensions")]
|
||||
|
||||
@@ -1438,7 +1442,7 @@ def create_ui():
|
||||
interface.render()
|
||||
|
||||
if opts.notification_audio_enable and os.path.exists(os.path.join(script_path, opts.notification_audio_path)):
|
||||
_audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, opts.notification_audio_path), elem_id="audio_notification", visible=False)
|
||||
gr.Audio(interactive=False, value=os.path.join(script_path, opts.notification_audio_path), elem_id="audio_notification", visible=False)
|
||||
|
||||
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
|
||||
settings_submit.click(
|
||||
|
||||
@@ -50,6 +50,7 @@ class Upscaler:
|
||||
return img
|
||||
|
||||
def upscale(self, img: PIL.Image, scale, selected_model: str = None):
|
||||
shared.log.debug(f'upscale: {img}|{scale}|{selected_model}')
|
||||
self.scale = scale
|
||||
dest_w = int(img.width * scale)
|
||||
dest_h = int(img.height * scale)
|
||||
|
||||
+78
-133
@@ -1,3 +1,5 @@
|
||||
# pylint: disable=unused-argument, attribute-defined-outside-init
|
||||
|
||||
import re
|
||||
import csv
|
||||
import random
|
||||
@@ -12,47 +14,36 @@ import modules.scripts as scripts
|
||||
import modules.shared as shared
|
||||
from modules import images, sd_samplers, processing, sd_models, sd_vae
|
||||
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
|
||||
from modules.shared import opts, state
|
||||
from modules.ui_components import ToolButton
|
||||
|
||||
fill_values_symbol = "\U0001f4d2" # 📒
|
||||
|
||||
AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
|
||||
|
||||
|
||||
def apply_field(field):
|
||||
def fun(p, x, xs):
|
||||
setattr(p, field, x)
|
||||
|
||||
return fun
|
||||
|
||||
|
||||
def apply_prompt(p, x, xs):
|
||||
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
|
||||
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
||||
|
||||
p.prompt = p.prompt.replace(xs[0], x)
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
shared.log.warning(f"XYZ grid: prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
||||
else:
|
||||
p.prompt = p.prompt.replace(xs[0], x)
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
|
||||
|
||||
def apply_order(p, x, xs):
|
||||
token_order = []
|
||||
|
||||
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
|
||||
for token in x:
|
||||
token_order.append((p.prompt.find(token), token))
|
||||
|
||||
token_order.sort(key=lambda t: t[0])
|
||||
|
||||
prompt_parts = []
|
||||
|
||||
# Split the prompt up, taking out the tokens
|
||||
for _, token in token_order:
|
||||
n = p.prompt.find(token)
|
||||
prompt_parts.append(p.prompt[0:n])
|
||||
p.prompt = p.prompt[n + len(token):]
|
||||
|
||||
# Rebuild the prompt with the tokens in the order we want
|
||||
prompt_tmp = ""
|
||||
for idx, part in enumerate(prompt_parts):
|
||||
prompt_tmp += part
|
||||
@@ -63,39 +54,42 @@ def apply_order(p, x, xs):
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if sampler_name is None:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
||||
p.sampler_name = sampler_name
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
p.sampler_name = sampler_name
|
||||
|
||||
|
||||
def confirm_samplers(p, xs):
|
||||
for x in xs:
|
||||
if x.lower() not in sd_samplers.samplers_map:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
|
||||
|
||||
def apply_checkpoint(p, x, xs):
|
||||
if x == shared.opts.sd_model_checkpoint:
|
||||
return
|
||||
info = sd_models.get_closet_checkpoint_match(x)
|
||||
if info is None:
|
||||
raise RuntimeError(f"Unknown checkpoint: {x}")
|
||||
sd_models.reload_model_weights(shared.sd_model, info)
|
||||
shared.log.warning(f"XYZ grid: unknown checkpoint: {x}")
|
||||
else:
|
||||
sd_models.reload_model_weights(shared.sd_model, info)
|
||||
|
||||
|
||||
def confirm_checkpoints(p, xs):
|
||||
for x in xs:
|
||||
if sd_models.get_closet_checkpoint_match(x) is None:
|
||||
raise RuntimeError(f"Unknown checkpoint: {x}")
|
||||
shared.log.warning(f"XYZ grid: Unknown checkpoint: {x}")
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
opts.data["CLIP_stop_at_last_layers"] = x
|
||||
shared.opts.data["CLIP_stop_at_last_layers"] = x
|
||||
|
||||
|
||||
def apply_upscale_latent_space(p, x, xs):
|
||||
if x.lower().strip() != '0':
|
||||
opts.data["use_scale_latent_for_hires_fix"] = True
|
||||
shared.opts.data["use_scale_latent_for_hires_fix"] = True
|
||||
else:
|
||||
opts.data["use_scale_latent_for_hires_fix"] = False
|
||||
shared.opts.data["use_scale_latent_for_hires_fix"] = False
|
||||
|
||||
|
||||
def find_vae(name: str):
|
||||
@@ -106,7 +100,7 @@ def find_vae(name: str):
|
||||
else:
|
||||
choices = [x for x in sorted(sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
|
||||
if len(choices) == 0:
|
||||
print(f"No VAE found for {name}; using automatic")
|
||||
shared.log.warning(f"No VAE found for {name}; using automatic")
|
||||
return sd_vae.unspecified
|
||||
else:
|
||||
return sd_vae.vae_dict[choices[0]]
|
||||
@@ -123,13 +117,13 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
|
||||
def apply_fallback(p, x, xs):
|
||||
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
|
||||
if sampler_name is None:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
||||
opts.data["xyz_fallback_sampler"] = sampler_name
|
||||
shared.log.warning(f"XYZ grid: unknown sampler: {x}")
|
||||
else:
|
||||
shared.opts.data["xyz_fallback_sampler"] = sampler_name
|
||||
|
||||
|
||||
def apply_uni_pc_order(p, x, xs):
|
||||
opts.data["uni_pc_order"] = min(x, p.steps - 1)
|
||||
shared.opts.data["uni_pc_order"] = min(x, p.steps - 1)
|
||||
|
||||
|
||||
def apply_face_restore(p, opt, x):
|
||||
@@ -142,23 +136,25 @@ def apply_face_restore(p, opt, x):
|
||||
p.face_restoration_model = 'GFPGAN'
|
||||
else:
|
||||
is_active = opt in ('true', 'yes', 'y', '1')
|
||||
|
||||
p.restore_faces = is_active
|
||||
|
||||
|
||||
def apply_token_merging_ratio_hr(p, x, xs):
|
||||
opts.data["token_merging_ratio_hr"] = x
|
||||
shared.opts.data["token_merging_ratio_hr"] = x
|
||||
|
||||
|
||||
def apply_token_merging_ratio(p, x, xs):
|
||||
opts.data["token_merging_ratio"] = x
|
||||
shared.opts.data["token_merging_ratio"] = x
|
||||
|
||||
|
||||
def apply_token_merging_random(p, x, xs):
|
||||
is_active = x.lower() in ('true', 'yes', 'y', '1')
|
||||
opts.data["token_merging_random"] = is_active
|
||||
shared.opts.data["token_merging_random"] = is_active
|
||||
|
||||
|
||||
def format_value_add_label(p, opt, x):
|
||||
if type(x) == float:
|
||||
x = round(x, 8)
|
||||
|
||||
return f"{opt.label}: {x}"
|
||||
|
||||
|
||||
@@ -186,11 +182,11 @@ def str_permutations(x):
|
||||
|
||||
|
||||
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, tipe, apply, fmt=format_value_add_label, confirm=None, cost=0.0, choices=None):
|
||||
self.label = label
|
||||
self.type = type
|
||||
self.type = tipe
|
||||
self.apply = apply
|
||||
self.format_value = format_value
|
||||
self.format_value = fmt
|
||||
self.confirm = confirm
|
||||
self.cost = cost
|
||||
self.choices = choices
|
||||
@@ -208,7 +204,7 @@ class AxisOptionTxt2Img(AxisOption):
|
||||
|
||||
|
||||
axis_options = [
|
||||
AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
|
||||
AxisOption("Nothing", str, do_nothing, fmt=format_nothing),
|
||||
AxisOption("Seed", int, apply_field("seed")),
|
||||
AxisOption("Var. seed", int, apply_field("subseed")),
|
||||
AxisOption("Var. strength", float, apply_field("subseed_strength")),
|
||||
@@ -216,11 +212,11 @@ axis_options = [
|
||||
AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
|
||||
AxisOption("CFG Scale", float, apply_field("cfg_scale")),
|
||||
AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
|
||||
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, fmt=format_value),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, fmt=format_value_join_list),
|
||||
AxisOptionTxt2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("Sampler", str, apply_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, fmt=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn")),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin")),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax")),
|
||||
@@ -229,12 +225,12 @@ axis_options = [
|
||||
AxisOption("Clip skip", int, apply_clip_skip),
|
||||
AxisOption("Denoising", float, apply_field("denoising_strength")),
|
||||
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
AxisOptionTxt2Img("Fallback latent upscaler sampler", str, apply_fallback, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionTxt2Img("Fallback latent upscaler sampler", str, apply_fallback, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
|
||||
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
|
||||
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
|
||||
AxisOption("Face restore", str, apply_face_restore, fmt=format_value),
|
||||
AxisOption("ToMe ratio",float,apply_token_merging_ratio),
|
||||
AxisOption("ToMe ratio for Hires fix",float,apply_token_merging_ratio_hr),
|
||||
AxisOption("ToMe random pertubations",str,apply_token_merging_random, choices = lambda: ["Yes","No"])
|
||||
@@ -245,12 +241,9 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
title_texts = [[images.GridAnnotation(z)] for z in z_labels]
|
||||
|
||||
list_size = (len(xs) * len(ys) * len(zs))
|
||||
|
||||
processed_result = None
|
||||
|
||||
state.job_count = list_size * p.n_iter
|
||||
shared.state.job_count = list_size * p.n_iter
|
||||
|
||||
def process_cell(x, y, z, ix, iy, iz):
|
||||
nonlocal processed_result
|
||||
@@ -258,10 +251,8 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
def index(ix, iy, iz):
|
||||
return ix + iy * len(xs) + iz * len(xs) * len(ys)
|
||||
|
||||
state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
|
||||
|
||||
shared.state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
|
||||
processed: Processed = cell(x, y, z, ix, iy, iz)
|
||||
|
||||
if processed_result is None:
|
||||
# Use our first processed result object as a template container to hold our full results
|
||||
processed_result = copy(processed)
|
||||
@@ -270,7 +261,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
processed_result.all_seeds = [None] * list_size
|
||||
processed_result.infotexts = [None] * list_size
|
||||
processed_result.index_of_first_image = 1
|
||||
|
||||
idx = index(ix, iy, iz)
|
||||
if processed.images:
|
||||
# Non-empty list indicates some degree of success.
|
||||
@@ -287,7 +277,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
cell_size = processed_result.images[0].size
|
||||
processed_result.images[idx] = Image.new(cell_mode, cell_size)
|
||||
|
||||
|
||||
if first_axes_processed == 'x':
|
||||
for ix, x in enumerate(xs):
|
||||
if second_axes_processed == 'y':
|
||||
@@ -321,14 +310,14 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
|
||||
if not processed_result:
|
||||
# Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
|
||||
print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
|
||||
shared.log.error("XYZ grid: Processing could not begin, you may need to refresh the tab or restart the service")
|
||||
return Processed(p, [])
|
||||
elif not any(processed_result.images):
|
||||
print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
|
||||
shared.log.error("XYZ grid: Failed to return even a single processed image")
|
||||
return Processed(p, [])
|
||||
|
||||
z_count = len(zs)
|
||||
sub_grids = [None] * z_count
|
||||
# sub_grids = [None] * z_count
|
||||
for i in range(z_count):
|
||||
start_index = (i * len(xs) * len(ys)) + i
|
||||
end_index = start_index + len(xs) * len(ys)
|
||||
@@ -339,7 +328,6 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
|
||||
processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
|
||||
processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
|
||||
|
||||
sub_grid_size = processed_result.images[0].size
|
||||
z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
|
||||
if draw_legend:
|
||||
@@ -348,36 +336,39 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
|
||||
#processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
|
||||
#processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
|
||||
processed_result.infotexts.insert(0, processed_result.infotexts[0])
|
||||
|
||||
return processed_result
|
||||
|
||||
|
||||
class SharedSettingsStackHelper(object):
|
||||
def __enter__(self):
|
||||
#Save overridden settings so they can be restored later.
|
||||
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
self.vae = opts.sd_vae
|
||||
self.uni_pc_order = opts.uni_pc_order
|
||||
self.token_merging_ratio_hr = opts.token_merging_ratio_hr
|
||||
self.token_merging_ratio = opts.token_merging_ratio
|
||||
self.token_merging_random = opts.token_merging_random
|
||||
self.CLIP_stop_at_last_layers = shared.opts.CLIP_stop_at_last_layers
|
||||
self.vae = shared.opts.sd_vae
|
||||
self.uni_pc_order = shared.opts.uni_pc_order
|
||||
self.token_merging_ratio_hr = shared.opts.token_merging_ratio_hr
|
||||
self.token_merging_ratio = shared.opts.token_merging_ratio
|
||||
self.token_merging_random = shared.opts.token_merging_random
|
||||
self.sd_model_checkpoint = shared.opts.sd_model_checkpoint
|
||||
self.sd_vae_checkpoint = shared.opts.sd_vae
|
||||
|
||||
def __exit__(self, exc_type, exc_value, tb):
|
||||
#Restore overriden settings after plot generation.
|
||||
opts.data["sd_vae"] = self.vae
|
||||
opts.data["uni_pc_order"] = self.uni_pc_order
|
||||
sd_models.reload_model_weights()
|
||||
sd_vae.reload_vae_weights()
|
||||
shared.opts.data["sd_vae"] = self.vae
|
||||
shared.opts.data["uni_pc_order"] = self.uni_pc_order
|
||||
shared.opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
|
||||
shared.opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr
|
||||
shared.opts.data["token_merging_ratio"] = self.token_merging_ratio
|
||||
shared.opts.data["token_merging_random"] = self.token_merging_random
|
||||
if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint:
|
||||
shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
|
||||
sd_models.reload_model_weights()
|
||||
if self.sd_vae_checkpoint != shared.opts.sd_vae:
|
||||
shared.opts.data["sd_vae"] = self.sd_vae_checkpoint
|
||||
sd_vae.reload_vae_weights()
|
||||
|
||||
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
|
||||
|
||||
opts.data["token_merging_ratio_hr"] = self.token_merging_ratio_hr
|
||||
opts.data["token_merging_ratio"] = self.token_merging_ratio
|
||||
opts.data["token_merging_random"] = self.token_merging_random
|
||||
|
||||
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
|
||||
|
||||
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
|
||||
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
|
||||
|
||||
@@ -388,7 +379,6 @@ class Script(scripts.Script):
|
||||
|
||||
def ui(self, is_img2img):
|
||||
self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=19):
|
||||
with gr.Row():
|
||||
@@ -408,7 +398,6 @@ class Script(scripts.Script):
|
||||
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
|
||||
z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
|
||||
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
|
||||
|
||||
with gr.Row(variant="compact", elem_id="axis_options"):
|
||||
draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
|
||||
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
|
||||
@@ -416,7 +405,6 @@ class Script(scripts.Script):
|
||||
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
|
||||
with gr.Row(variant="compact", elem_id="axis_options"):
|
||||
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
|
||||
with gr.Row(variant="compact", elem_id="swap_axes"):
|
||||
swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
|
||||
swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
|
||||
@@ -475,25 +463,21 @@ class Script(scripts.Script):
|
||||
|
||||
return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
|
||||
|
||||
def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
|
||||
def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size): # pylint: disable=arguments-differ
|
||||
shared.log.debug(f'xyzgrid: {x_type}|{x_values}|{x_values_dropdown}|{y_type}|{y_values}|{y_values_dropdown}|{z_type}|{z_values}|{z_values_dropdown}|{draw_legend}|{include_lone_images}|{include_sub_grids}|{no_fixed_seeds}|{margin_size}')
|
||||
if not no_fixed_seeds:
|
||||
processing.fix_seed(p)
|
||||
|
||||
if not opts.return_grid:
|
||||
if not shared.opts.return_grid:
|
||||
p.batch_size = 1
|
||||
|
||||
def process_axis(opt, vals, vals_dropdown):
|
||||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
|
||||
if opt.choices is not None:
|
||||
valslist = vals_dropdown
|
||||
else:
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
|
||||
|
||||
if opt.type == int:
|
||||
valslist_ext = []
|
||||
|
||||
for val in valslist:
|
||||
m = re_range.fullmatch(val)
|
||||
mc = re_range_count.fullmatch(val)
|
||||
@@ -501,21 +485,17 @@ class Script(scripts.Script):
|
||||
start = int(m.group(1))
|
||||
end = int(m.group(2))+1
|
||||
step = int(m.group(3)) if m.group(3) is not None else 1
|
||||
|
||||
valslist_ext += list(range(start, end, step))
|
||||
elif mc is not None:
|
||||
start = int(mc.group(1))
|
||||
end = int(mc.group(2))
|
||||
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
||||
|
||||
valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
|
||||
else:
|
||||
valslist_ext.append(val)
|
||||
|
||||
valslist = valslist_ext
|
||||
elif opt.type == float:
|
||||
valslist_ext = []
|
||||
|
||||
for val in valslist:
|
||||
m = re_range_float.fullmatch(val)
|
||||
mc = re_range_count_float.fullmatch(val)
|
||||
@@ -523,48 +503,38 @@ class Script(scripts.Script):
|
||||
start = float(m.group(1))
|
||||
end = float(m.group(2))
|
||||
step = float(m.group(3)) if m.group(3) is not None else 1
|
||||
|
||||
valslist_ext += np.arange(start, end + step, step).tolist()
|
||||
elif mc is not None:
|
||||
start = float(mc.group(1))
|
||||
end = float(mc.group(2))
|
||||
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
||||
|
||||
valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
|
||||
else:
|
||||
valslist_ext.append(val)
|
||||
|
||||
valslist = valslist_ext
|
||||
elif opt.type == str_permutations:
|
||||
valslist = list(permutations(valslist))
|
||||
|
||||
valslist = [opt.type(x) for x in valslist]
|
||||
|
||||
# Confirm options are valid before starting
|
||||
if opt.confirm:
|
||||
opt.confirm(p, valslist)
|
||||
|
||||
return valslist
|
||||
|
||||
x_opt = self.current_axis_options[x_type]
|
||||
if x_opt.choices is not None:
|
||||
x_values = ",".join(x_values_dropdown)
|
||||
xs = process_axis(x_opt, x_values, x_values_dropdown)
|
||||
|
||||
y_opt = self.current_axis_options[y_type]
|
||||
if y_opt.choices is not None:
|
||||
y_values = ",".join(y_values_dropdown)
|
||||
ys = process_axis(y_opt, y_values, y_values_dropdown)
|
||||
|
||||
z_opt = self.current_axis_options[z_type]
|
||||
if z_opt.choices is not None:
|
||||
z_values = ",".join(z_values_dropdown)
|
||||
zs = process_axis(z_opt, z_values, z_values_dropdown)
|
||||
|
||||
# this could be moved to common code, but unlikely to be ever triggered anywhere else
|
||||
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
|
||||
grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
|
||||
assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
|
||||
assert grid_mp < shared.opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {shared.opts.img_max_size_mp} MPixels)'
|
||||
|
||||
def fix_axis_seeds(axis_opt, axis_list):
|
||||
if axis_opt.label in ['Seed', 'Var. seed']:
|
||||
@@ -585,7 +555,6 @@ class Script(scripts.Script):
|
||||
total_steps = sum(zs) * len(xs) * len(ys)
|
||||
else:
|
||||
total_steps = p.steps * len(xs) * len(ys) * len(zs)
|
||||
|
||||
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
|
||||
if x_opt.label == "Hires steps":
|
||||
total_steps += sum(xs) * len(ys) * len(zs)
|
||||
@@ -597,21 +566,13 @@ class Script(scripts.Script):
|
||||
total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
|
||||
else:
|
||||
total_steps *= 2
|
||||
|
||||
total_steps *= p.n_iter
|
||||
|
||||
image_cell_count = p.n_iter * p.batch_size
|
||||
cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
|
||||
plural_s = 's' if len(zs) > 1 else ''
|
||||
print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
|
||||
|
||||
state.xyz_plot_x = AxisInfo(x_opt, xs)
|
||||
state.xyz_plot_y = AxisInfo(y_opt, ys)
|
||||
state.xyz_plot_z = AxisInfo(z_opt, zs)
|
||||
|
||||
# If one of the axes is very slow to change between (like SD model
|
||||
# checkpoint), then make sure it is in the outer iteration of the nested
|
||||
# `for` loop.
|
||||
shared.log.info(f"XYZ grid: images={len(xs)*len(ys)*len(zs)*image_cell_count} grid={len(zs)} {len(xs)}x{len(ys)} cells={len(zs)} steps={total_steps}")
|
||||
shared.state.xyz_plot_x = AxisInfo(x_opt, xs)
|
||||
shared.state.xyz_plot_y = AxisInfo(y_opt, ys)
|
||||
shared.state.xyz_plot_z = AxisInfo(z_opt, zs)
|
||||
# If one of the axes is very slow to change between (like SD model checkpoint), then make sure it is in the outer iteration of the nested `for` loop.
|
||||
first_axes_processed = 'z'
|
||||
second_axes_processed = 'y'
|
||||
if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
|
||||
@@ -632,41 +593,33 @@ class Script(scripts.Script):
|
||||
second_axes_processed = 'x'
|
||||
else:
|
||||
second_axes_processed = 'y'
|
||||
|
||||
grid_infotext = [None] * (1 + len(zs))
|
||||
|
||||
def cell(x, y, z, ix, iy, iz):
|
||||
if shared.state.interrupted:
|
||||
return Processed(p, [], p.seed, "")
|
||||
|
||||
pc = copy(p)
|
||||
pc.styles = pc.styles[:]
|
||||
x_opt.apply(pc, x, xs)
|
||||
y_opt.apply(pc, y, ys)
|
||||
z_opt.apply(pc, z, zs)
|
||||
|
||||
res = process_images(pc)
|
||||
|
||||
# Sets subgrid infotexts
|
||||
subgrid_index = 1 + iz
|
||||
if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
|
||||
pc.extra_generation_params = copy(pc.extra_generation_params)
|
||||
pc.extra_generation_params['Script'] = self.title()
|
||||
|
||||
if x_opt.label != 'Nothing':
|
||||
pc.extra_generation_params["X Type"] = x_opt.label
|
||||
pc.extra_generation_params["X Values"] = x_values
|
||||
if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
|
||||
pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
|
||||
|
||||
if y_opt.label != 'Nothing':
|
||||
pc.extra_generation_params["Y Type"] = y_opt.label
|
||||
pc.extra_generation_params["Y Values"] = y_values
|
||||
if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
|
||||
pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
|
||||
|
||||
grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
|
||||
|
||||
# Sets main grid infotext
|
||||
if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
|
||||
pc.extra_generation_params = copy(pc.extra_generation_params)
|
||||
@@ -676,9 +629,7 @@ class Script(scripts.Script):
|
||||
pc.extra_generation_params["Z Values"] = z_values
|
||||
if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
|
||||
pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
|
||||
|
||||
grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
|
||||
|
||||
return res
|
||||
|
||||
with SharedSettingsStackHelper():
|
||||
@@ -702,23 +653,18 @@ class Script(scripts.Script):
|
||||
if not processed.images:
|
||||
# It broke, no further handling needed.
|
||||
return processed
|
||||
|
||||
z_count = len(zs)
|
||||
|
||||
# Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
|
||||
processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
|
||||
|
||||
if not include_lone_images:
|
||||
# Don't need sub-images anymore, drop from list:
|
||||
processed.images = processed.images[:z_count+1]
|
||||
|
||||
if opts.grid_save:
|
||||
if shared.opts.grid_save:
|
||||
# Auto-save main and sub-grids:
|
||||
grid_count = z_count + 1 if z_count > 1 else 1
|
||||
for g in range(grid_count):
|
||||
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)
|
||||
|
||||
images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=shared.opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
|
||||
if not include_sub_grids:
|
||||
# Done with sub-grids, drop all related information:
|
||||
for _sg in range(z_count):
|
||||
@@ -726,5 +672,4 @@ class Script(scripts.Script):
|
||||
del processed.all_prompts[1]
|
||||
del processed.all_seeds[1]
|
||||
del processed.infotexts[1]
|
||||
|
||||
return processed
|
||||
|
||||
@@ -155,8 +155,8 @@ def load_model():
|
||||
log.error("Stable diffusion model failed to load")
|
||||
exit(1)
|
||||
if shared.sd_model is None:
|
||||
log.error("No stable diffusion model loaded")
|
||||
exit(1)
|
||||
log.warning("No stable diffusion model loaded")
|
||||
# exit(1)
|
||||
else:
|
||||
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
|
||||
|
||||
Reference in New Issue
Block a user