From 76ef3d75f61253516c024553335d9083d9660a8a Mon Sep 17 00:00:00 2001 From: JC_Array Date: Mon, 10 Oct 2022 18:01:49 -0500 Subject: added deepbooru settings (threshold and sort by alpha or likelyhood) --- modules/shared.py | 6 ++++++ 1 file changed, 6 insertions(+) (limited to 'modules/shared.py') diff --git a/modules/shared.py b/modules/shared.py index 1995a99a..2e307809 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -261,6 +261,12 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), })) +if cmd_opts.deepdanbooru: + options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), { + "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox), + 'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), + })) + class Options: data = None -- cgit v1.2.3 From c0484f1b986ce7acb0e3596f6089a191279f5442 Mon Sep 17 00:00:00 2001 From: brkirch Date: Mon, 10 Oct 2022 22:48:54 -0400 Subject: Add cross-attention optimization from InvokeAI * Add cross-attention optimization from InvokeAI (~30% speed improvement on MPS) * Add command line option for it * Make it default when CUDA is unavailable --- modules/sd_hijack.py | 5 ++- modules/sd_hijack_optimizations.py | 79 ++++++++++++++++++++++++++++++++++++++ modules/shared.py | 5 ++- 3 files changed, 86 insertions(+), 3 deletions(-) (limited to 'modules/shared.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index f07ec041..5a1b167f 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -30,8 +30,11 @@ def apply_optimizations(): elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): + print("Applying cross attention optimization (InvokeAI).") + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): - print("Applying cross attention optimization.") + print("Applying cross attention optimization (Doggettx).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 3349b9c3..870226c5 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,6 +1,7 @@ import math import sys import traceback +import psutil import torch from torch import einsum @@ -116,6 +117,84 @@ def split_cross_attention_forward(self, x, context=None, mask=None): return self.to_out(r2) +# -- From https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py (with hypernetworks support added) -- + +mem_total_gb = psutil.virtual_memory().total // (1 << 30) + +def einsum_op_compvis(q, k, v): + s = einsum('b i d, b j d -> b i j', q, k) + s = s.softmax(dim=-1, dtype=s.dtype) + return einsum('b i j, b j d -> b i d', s, v) + +def einsum_op_slice_0(q, k, v, slice_size): + r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + for i in range(0, q.shape[0], slice_size): + end = i + slice_size + r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end]) + return r + +def einsum_op_slice_1(q, k, v, slice_size): + r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v) + return r + +def einsum_op_mps_v1(q, k, v): + if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096 + return einsum_op_compvis(q, k, v) + else: + slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1])) + return einsum_op_slice_1(q, k, v, slice_size) + +def einsum_op_mps_v2(q, k, v): + if mem_total_gb > 8 and q.shape[1] <= 4096: + return einsum_op_compvis(q, k, v) + else: + return einsum_op_slice_0(q, k, v, 1) + +def einsum_op_tensor_mem(q, k, v, max_tensor_mb): + size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20) + if size_mb <= max_tensor_mb: + return einsum_op_compvis(q, k, v) + div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length() + if div <= q.shape[0]: + return einsum_op_slice_0(q, k, v, q.shape[0] // div) + return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1)) + +def einsum_op(q, k, v): + if q.device.type == 'mps': + if mem_total_gb >= 32: + return einsum_op_mps_v1(q, k, v) + return einsum_op_mps_v2(q, k, v) + + # Smaller slices are faster due to L2/L3/SLC caches. + # Tested on i7 with 8MB L3 cache. + return einsum_op_tensor_mem(q, k, v, 32) + +def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + + hypernetwork = shared.loaded_hypernetwork + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + + if hypernetwork_layers is not None: + k = self.to_k(hypernetwork_layers[0](context)) * self.scale + v = self.to_v(hypernetwork_layers[1](context)) + else: + k = self.to_k(context) * self.scale + v = self.to_v(context) + del context, x + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + r = einsum_op(q, k, v) + return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h)) + +# -- End of code from https://github.com/invoke-ai/InvokeAI/blob/main/ldm/modules/attention.py -- + def xformers_attention_forward(self, x, context=None, mask=None): h = self.heads q_in = self.to_q(x) diff --git a/modules/shared.py b/modules/shared.py index 1dc2ccf2..20b45f23 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -50,9 +50,10 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator") -parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.") -parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") +parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.") +parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") +parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[]) parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) -- cgit v1.2.3 From d4ea5f4d8631f778d11efcde397e4a5b8801d43b Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 11 Oct 2022 19:03:08 +0300 Subject: add an option to unload models during hypernetwork training to save VRAM --- modules/hypernetworks/hypernetwork.py | 25 +++++++++++++++------- modules/hypernetworks/ui.py | 4 +++- modules/shared.py | 4 ++++ modules/textual_inversion/dataset.py | 29 ++++++++++++++++++-------- modules/textual_inversion/textual_inversion.py | 2 +- 5 files changed, 46 insertions(+), 18 deletions(-) (limited to 'modules/shared.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b081f14e..4700e1ec 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -175,6 +175,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) + unload = shared.opts.unload_models_when_training if save_hypernetwork_every > 0: hypernetwork_dir = os.path.join(log_directory, "hypernetworks") @@ -188,11 +189,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, else: images_dir = None - cond_model = shared.sd_model.cond_stage_model - shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True) + + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) hypernetwork = shared.loaded_hypernetwork weights = hypernetwork.weights() @@ -211,7 +214,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, return hypernetwork, filename pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, (x, text) in pbar: + for i, (x, text, cond) in pbar: hypernetwork.step = i + ititial_step if hypernetwork.step > steps: @@ -221,11 +224,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, break with torch.autocast("cuda"): - c = cond_model([text]) - + cond = cond.to(devices.device) x = x.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] + loss = shared.sd_model(x.unsqueeze(0), cond)[0] del x + del cond losses[hypernetwork.step % losses.shape[0]] = loss.item() @@ -244,6 +247,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, preview_text = text if preview_image_prompt == "" else preview_image_prompt + optimizer.zero_grad() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, prompt=preview_text, @@ -255,6 +262,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, processed = processing.process_images(p) image = processed.images[0] + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) + shared.state.current_image = image image.save(last_saved_image) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 3541a388..c67facbb 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -5,7 +5,7 @@ import gradio as gr import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess -from modules import sd_hijack, shared +from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork @@ -41,5 +41,7 @@ Hypernetwork saved to {html.escape(filename)} raise finally: shared.loaded_hypernetwork = initial_hypernetwork + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) sd_hijack.apply_optimizations() diff --git a/modules/shared.py b/modules/shared.py index 20b45f23..c1092ff7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -228,6 +228,10 @@ options_templates.update(options_section(('system', "System"), { "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), })) +options_templates.update(options_section(('training', "Training"), { + "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"), +})) + options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True), "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}), diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 4d006366..f61f40d3 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -8,14 +8,14 @@ from torchvision import transforms import random import tqdm -from modules import devices +from modules import devices, shared import re re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset): - def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False): self.placeholder_token = placeholder_token @@ -32,6 +32,8 @@ class PersonalizedBase(Dataset): assert data_root, 'dataset directory not specified' + cond_model = shared.sd_model.cond_stage_model + self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): @@ -53,7 +55,13 @@ class PersonalizedBase(Dataset): init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() init_latent = init_latent.to(devices.cpu) - self.dataset.append((init_latent, filename_tokens)) + if include_cond: + text = self.create_text(filename_tokens) + cond = cond_model([text]).to(devices.cpu) + else: + cond = None + + self.dataset.append((init_latent, filename_tokens, cond)) self.length = len(self.dataset) * repeats @@ -64,6 +72,12 @@ class PersonalizedBase(Dataset): def shuffle(self): self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] + def create_text(self, filename_tokens): + text = random.choice(self.lines) + text = text.replace("[name]", self.placeholder_token) + text = text.replace("[filewords]", ' '.join(filename_tokens)) + return text + def __len__(self): return self.length @@ -72,10 +86,7 @@ class PersonalizedBase(Dataset): self.shuffle() index = self.indexes[i % len(self.indexes)] - x, filename_tokens = self.dataset[index] - - text = random.choice(self.lines) - text = text.replace("[name]", self.placeholder_token) - text = text.replace("[filewords]", ' '.join(filename_tokens)) + x, filename_tokens, cond = self.dataset[index] - return x, text + text = self.create_text(filename_tokens) + return x, text, cond diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index bb05cdc6..35f4bd9e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -201,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini return embedding, filename pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) - for i, (x, text) in pbar: + for i, (x, text, _) in pbar: embedding.step = i + ititial_step if embedding.step > steps: -- cgit v1.2.3 From f53f703aebc801c4204182d52bb1e0bef9808e1f Mon Sep 17 00:00:00 2001 From: JC_Array Date: Tue, 11 Oct 2022 18:12:12 -0500 Subject: resolved conflicts, moved settings under interrogate section, settings only show if deepbooru flag is enabled --- modules/deepbooru.py | 2 +- modules/shared.py | 19 +++++++++---------- modules/textual_inversion/preprocess.py | 2 +- modules/ui.py | 2 +- 4 files changed, 12 insertions(+), 13 deletions(-) (limited to 'modules/shared.py') diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 89dcac3c..29529949 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -8,7 +8,7 @@ def get_deepbooru_tags(pil_image): This method is for running only one image at a time for simple use. Used to the img2img interrogate. """ from modules import shared # prevents circular reference - create_deepbooru_process(shared.opts.deepbooru_threshold, shared.opts.deepbooru_sort_alpha) + create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, shared.opts.deepbooru_sort_alpha) shared.deepbooru_process_return["value"] = -1 shared.deepbooru_process_queue.put(pil_image) while shared.deepbooru_process_return["value"] == -1: diff --git a/modules/shared.py b/modules/shared.py index 817203f8..5456c477 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -248,15 +248,20 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -options_templates.update(options_section(('interrogate', "Interrogate Options"), { +interrogate_option_dictionary = { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), - "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"), - "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), -})) + "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)") +} + +if cmd_opts.deepdanbooru: + interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}) + interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox) + +options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary)) options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), @@ -282,12 +287,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), })) -if cmd_opts.deepdanbooru: - options_templates.update(options_section(('deepbooru-params', "DeepBooru parameters"), { - "deepbooru_sort_alpha": OptionInfo(True, "Sort Alphabetical", gr.Checkbox), - 'deepbooru_threshold': OptionInfo(0.5, "Threshold", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), - })) - class Options: data = None diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index a96388d6..113cecf1 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -29,7 +29,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ shared.interrogator.load() if process_caption_deepbooru: - deepbooru.create_deepbooru_process(opts.deepbooru_threshold, opts.deepbooru_sort_alpha) + deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, opts.deepbooru_sort_alpha) def save_pic_with_caption(image, index): if process_caption: diff --git a/modules/ui.py b/modules/ui.py index 2891fc8c..fa45edca 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -317,7 +317,7 @@ def interrogate(image): def interrogate_deepbooru(image): - prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold) + prompt = get_deepbooru_tags(image) return gr_show(True) if prompt is None else prompt -- cgit v1.2.3 From 65b973ac4e547a325f30a05f852b161421af2041 Mon Sep 17 00:00:00 2001 From: supersteve3d <39339941+supersteve3d@users.noreply.github.com> Date: Wed, 12 Oct 2022 08:21:52 +0800 Subject: Update shared.py Correct typo to "Unload VAE and CLIP from VRAM when training" in settings tab. --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/shared.py') diff --git a/modules/shared.py b/modules/shared.py index c1092ff7..46bc740c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -229,7 +229,7 @@ options_templates.update(options_section(('system', "System"), { })) options_templates.update(options_section(('training', "Training"), { - "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"), + "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), })) options_templates.update(options_section(('sd', "Stable Diffusion"), { -- cgit v1.2.3 From 6ac2ec2b78bc5fabd09cb866dd9a71061d669269 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 07:01:20 +0300 Subject: create dir for hypernetworks --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules/shared.py') diff --git a/modules/shared.py b/modules/shared.py index c1092ff7..e65e77f8 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -86,6 +86,7 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file +os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None -- cgit v1.2.3 From 336bd8703c7b4d71f2f096f303599925a30b8167 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 12 Oct 2022 09:00:07 +0300 Subject: just add the deepdanbooru settings unconditionally --- modules/shared.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) (limited to 'modules/shared.py') diff --git a/modules/shared.py b/modules/shared.py index f150e024..42e99741 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -249,20 +249,15 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) -interrogate_option_dictionary = { +options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), - "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)") -} - -if cmd_opts.deepdanbooru: - interrogate_option_dictionary["interrogate_deepbooru_score_threshold"] = OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}) - interrogate_option_dictionary["deepbooru_sort_alpha"] = OptionInfo(True, "Interrogate: deepbooru sort alphabetically", gr.Checkbox) - -options_templates.update(options_section(('interrogate', "Interrogate Options"), interrogate_option_dictionary)) + "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), + "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"), +})) options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), -- cgit v1.2.3