From 29e74d6e71826da9a3fe3c5790fed1329fc4d1e8 Mon Sep 17 00:00:00 2001 From: Melan Date: Thu, 20 Oct 2022 16:26:16 +0200 Subject: Add support for Tensorboard for training embeddings --- modules/textual_inversion/textual_inversion.py | 31 +++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3be69562..c57d3ace 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -7,9 +7,11 @@ import tqdm import html import datetime import csv +import numpy as np +import torchvision.transforms from PIL import Image, PngImagePlugin - +from torch.utils.tensorboard import SummaryWriter from modules import shared, devices, sd_hijack, processing, sd_models import modules.textual_inversion.dataset from modules.textual_inversion.learn_schedule import LearnRateScheduler @@ -199,6 +201,19 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +def tensorboard_add_scaler(tensorboard_writer, tag, value, step): + if shared.opts.training_enable_tensorboard: + tensorboard_writer.add_scalar(tag=tag, + scalar_value=value, global_step=step) + +def tensorboard_add_image(tensorboard_writer, tag, pil_image, step): + if shared.opts.training_enable_tensorboard: + # Convert a pil image to a torch tensor + img_tensor = torch.as_tensor(np.array(pil_image, copy=True)) + img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands())) + img_tensor = img_tensor.permute((2, 0, 1)) + + tensorboard_writer.add_image(tag, img_tensor, global_step=step) def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' @@ -252,6 +267,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) + if shared.opts.training_enable_tensorboard: + os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True) + tensorboard_writer = SummaryWriter( + log_dir=os.path.join(log_directory, "tensorboard"), + flush_secs=shared.opts.training_tensorboard_flush_every) + pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, entries in pbar: embedding.step = i + ititial_step @@ -270,6 +291,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc del x losses[embedding.step % losses.shape[0]] = loss.item() + optimizer.zero_grad() loss.backward() @@ -285,6 +307,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding.save(last_saved_file) embedding_yet_to_be_embedded = True + if shared.opts.training_enable_tensorboard: + tensorboard_add_scaler(tensorboard_writer, "Loss/train", losses.mean(), embedding.step) + tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", losses.mean(), epoch_step) + tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", scheduler.learn_rate, embedding.step) + tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", scheduler.learn_rate, epoch_step) + write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { "loss": f"{losses.mean():.7f}", "learn_rate": scheduler.learn_rate @@ -349,6 +377,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding_yet_to_be_embedded = False image.save(last_saved_image) + tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step) last_saved_image += f", prompt: {preview_text}" -- cgit v1.2.3 From a6d593a6b51dc6a8443f2aa5c24caa391a04cd56 Mon Sep 17 00:00:00 2001 From: Melan Date: Thu, 20 Oct 2022 19:43:21 +0200 Subject: Fixed a typo in a variable --- modules/textual_inversion/textual_inversion.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c57d3ace..ec8176bf 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -260,11 +260,11 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc last_saved_image = "" embedding_yet_to_be_embedded = False - ititial_step = embedding.step or 0 - if ititial_step > steps: + initial_step = embedding.step or 0 + if initial_step > steps: return embedding, filename - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + scheduler = LearnRateScheduler(learn_rate, steps, initial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) if shared.opts.training_enable_tensorboard: @@ -273,9 +273,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc log_dir=os.path.join(log_directory, "tensorboard"), flush_secs=shared.opts.training_tensorboard_flush_every) - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + pbar = tqdm.tqdm(enumerate(ds), total=steps-initial_step) for i, entries in pbar: - embedding.step = i + ititial_step + embedding.step = i + initial_step scheduler.apply(optimizer, embedding.step) if scheduler.finished: -- cgit v1.2.3 From 8f5912984794c4c69e429c4636e984854d911b6a Mon Sep 17 00:00:00 2001 From: Melan Date: Thu, 20 Oct 2022 22:37:16 +0200 Subject: Some changes to the tensorboard code and hypernetwork support --- modules/hypernetworks/hypernetwork.py | 18 ++++++++++- modules/textual_inversion/textual_inversion.py | 45 +++++++++++++++----------- 2 files changed, 44 insertions(+), 19 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 74300122..5e919775 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -4,6 +4,7 @@ import html import os import sys import traceback +import tensorboard import tqdm import csv @@ -18,7 +19,6 @@ import modules.textual_inversion.dataset from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler - class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -291,6 +291,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + if shared.opts.training_enable_tensorboard: + tensorboard_writer = textual_inversion.tensorboard_setup(log_directory) + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step @@ -315,6 +318,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log optimizer.zero_grad() loss.backward() optimizer.step() + mean_loss = losses.mean() if torch.isnan(mean_loss): raise RuntimeError("Loss diverged.") @@ -323,6 +327,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') hypernetwork.save(last_saved_file) + + if shared.opts.training_enable_tensorboard: + epoch_num = hypernetwork.step // len(ds) + epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1 + + textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, + global_step=hypernetwork.step, step=epoch_step, + learn_rate=scheduler.learn_rate, epoch_num=epoch_num) textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { "loss": f"{mean_loss:.7f}", @@ -360,6 +372,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log processed = processing.process_images(p) image = processed.images[0] if len(processed.images)>0 else None + if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: + textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", + image, hypernetwork.step) + if unload: shared.sd_model.cond_stage_model.to(devices.cpu) shared.sd_model.first_stage_model.to(devices.cpu) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ec8176bf..b1dc2596 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -201,19 +201,30 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +def tensorboard_setup(log_directory): + os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True) + return SummaryWriter( + log_dir=os.path.join(log_directory, "tensorboard"), + flush_secs=shared.opts.training_tensorboard_flush_every) + +def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num): + tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step) + tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step) + tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step) + tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step) + def tensorboard_add_scaler(tensorboard_writer, tag, value, step): - if shared.opts.training_enable_tensorboard: - tensorboard_writer.add_scalar(tag=tag, - scalar_value=value, global_step=step) + tensorboard_writer.add_scalar(tag=tag, + scalar_value=value, global_step=step) def tensorboard_add_image(tensorboard_writer, tag, pil_image, step): - if shared.opts.training_enable_tensorboard: - # Convert a pil image to a torch tensor - img_tensor = torch.as_tensor(np.array(pil_image, copy=True)) - img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands())) - img_tensor = img_tensor.permute((2, 0, 1)) + # Convert a pil image to a torch tensor + img_tensor = torch.as_tensor(np.array(pil_image, copy=True)) + img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], + len(pil_image.getbands())) + img_tensor = img_tensor.permute((2, 0, 1)) - tensorboard_writer.add_image(tag, img_tensor, global_step=step) + tensorboard_writer.add_image(tag, img_tensor, global_step=step) def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' @@ -268,10 +279,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) if shared.opts.training_enable_tensorboard: - os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True) - tensorboard_writer = SummaryWriter( - log_dir=os.path.join(log_directory, "tensorboard"), - flush_secs=shared.opts.training_tensorboard_flush_every) + tensorboard_writer = tensorboard_setup(log_directory) pbar = tqdm.tqdm(enumerate(ds), total=steps-initial_step) for i, entries in pbar: @@ -308,10 +316,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding_yet_to_be_embedded = True if shared.opts.training_enable_tensorboard: - tensorboard_add_scaler(tensorboard_writer, "Loss/train", losses.mean(), embedding.step) - tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", losses.mean(), epoch_step) - tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", scheduler.learn_rate, embedding.step) - tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", scheduler.learn_rate, epoch_step) + tensorboard_add(tensorboard_writer, loss=losses.mean(), global_step=embedding.step, + step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num) write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), { "loss": f"{losses.mean():.7f}", @@ -377,7 +383,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc embedding_yet_to_be_embedded = False image.save(last_saved_image) - tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step) + + if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: + tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", + image, embedding.step) last_saved_image += f", prompt: {preview_text}" -- cgit v1.2.3 From 18f86e41f6f289042c075bff1498e620ab997b8c Mon Sep 17 00:00:00 2001 From: Melan Date: Mon, 24 Oct 2022 17:21:18 +0200 Subject: Removed two unused imports --- modules/hypernetworks/hypernetwork.py | 1 - modules/textual_inversion/textual_inversion.py | 1 - 2 files changed, 2 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 0cd94f49..2263e95e 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -4,7 +4,6 @@ import html import os import sys import traceback -import tensorboard import tqdm import csv diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index b1dc2596..589314fe 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -9,7 +9,6 @@ import datetime import csv import numpy as np -import torchvision.transforms from PIL import Image, PngImagePlugin from torch.utils.tensorboard import SummaryWriter from modules import shared, devices, sd_hijack, processing, sd_models -- cgit v1.2.3 From 1618df41bad092e068c61bf510b1e20856821ad5 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Fri, 28 Oct 2022 10:31:27 +0700 Subject: Gradient clipping for textual embedding --- modules/textual_inversion/textual_inversion.py | 11 ++++++++++- modules/ui.py | 2 ++ 2 files changed, 12 insertions(+), 1 deletion(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ff002d3e..7bad73a6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -206,7 +206,7 @@ def write_loss(log_directory, filename, step, epoch_len, values): }) -def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -256,6 +256,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if ititial_step > steps: return embedding, filename + clip_grad_mode_value = clip_grad_mode == "value" + clip_grad_mode_norm = clip_grad_mode == "norm" + scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) @@ -280,6 +283,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc optimizer.zero_grad() loss.backward() + + if clip_grad_mode_value: + torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value) + elif clip_grad_mode_norm: + torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value) + optimizer.step() diff --git a/modules/ui.py b/modules/ui.py index ba5e92a7..97de7da2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1409,6 +1409,8 @@ def create_ui(wrap_gradio_gpu_call): training_width, training_height, steps, + clip_grad_mode, + clip_grad_value, create_image_every, save_embedding_every, template_file, -- cgit v1.2.3 From 16451ca573220e49f2eaaab97580b6b91287c8c4 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Fri, 28 Oct 2022 17:16:23 +0700 Subject: Learning rate sched syntax support for grad clipping --- modules/hypernetworks/hypernetwork.py | 13 ++++++++++--- modules/textual_inversion/learn_schedule.py | 11 ++++++++--- modules/textual_inversion/textual_inversion.py | 12 +++++++++--- modules/ui.py | 7 +++---- 4 files changed, 30 insertions(+), 13 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index c5d60654..86532063 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -383,11 +383,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log ititial_step = hypernetwork.step or 0 if ititial_step > steps: return hypernetwork, filename - + clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" + clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm + if clip_grad_enabled: + clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) + # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) @@ -407,6 +411,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if shared.state.interrupted: break + if clip_grad_enabled: + clip_grad_sched.step(hypernetwork.step) + with torch.autocast("cuda"): c = stack_conds([entry.cond for entry in entries]).to(devices.device) # c = torch.vstack([entry.cond for entry in entries]).to(devices.device) @@ -430,9 +437,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue' if clip_grad_mode_value: - torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_value) + torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_sched.learn_rate) elif clip_grad_mode_norm: - torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_value) + torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_sched.learn_rate) optimizer.step() diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py index 2062726a..ffec3e1b 100644 --- a/modules/textual_inversion/learn_schedule.py +++ b/modules/textual_inversion/learn_schedule.py @@ -51,14 +51,19 @@ class LearnRateScheduler: self.finished = False - def apply(self, optimizer, step_number): + def step(self, step_number): if step_number <= self.end_step: - return + return False try: (self.learn_rate, self.end_step) = next(self.schedules) - except Exception: + except StopIteration: self.finished = True + return False + return True + + def apply(self, optimizer, step_number): + if not self.step(step_number): return if self.verbose: diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 7bad73a6..6b00c6a1 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -255,9 +255,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc ititial_step = embedding.step or 0 if ititial_step > steps: return embedding, filename - + clip_grad_mode_value = clip_grad_mode == "value" clip_grad_mode_norm = clip_grad_mode == "norm" + clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm + if clip_grad_enabled: + clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) @@ -273,6 +276,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if shared.state.interrupted: break + if clip_grad_enabled: + clip_grad_sched.step(embedding.step) + with torch.autocast("cuda"): c = cond_model([entry.cond_text for entry in entries]) x = torch.stack([entry.latent for entry in entries]).to(devices.device) @@ -285,9 +291,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc loss.backward() if clip_grad_mode_value: - torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_value) + torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_sched.learn_rate) elif clip_grad_mode_norm: - torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_value) + torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_sched.learn_rate) optimizer.step() diff --git a/modules/ui.py b/modules/ui.py index 97de7da2..47d16429 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1305,7 +1305,9 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005") hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001") - + with gr.Row(): + clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) + clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="1.0", show_label=False) batch_size = gr.Number(label='Batch size', value=1, precision=0) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") @@ -1313,9 +1315,6 @@ def create_ui(wrap_gradio_gpu_call): training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) steps = gr.Number(label='Max steps', value=100000, precision=0) - with gr.Row(): - clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) - clip_grad_value = gr.Number(value=1.0, show_label=False) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) -- cgit v1.2.3 From cffc240a7327ae60671ff533469fc4ed4bf605de Mon Sep 17 00:00:00 2001 From: Nerogar Date: Sun, 23 Oct 2022 14:05:25 +0200 Subject: fixed textual inversion training with inpainting models --- modules/textual_inversion/textual_inversion.py | 27 +++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 0aeb0459..2630c7c9 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -224,6 +224,26 @@ def validate_train_inputs(model_name, learn_rate, batch_size, data_root, templat if save_model_every or create_image_every: assert log_directory, "Log directory is empty" +def create_dummy_mask(x, width=None, height=None): + if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}: + + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) + image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning)) + + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) + + else: + # Dummy zero conditioning if we're not using inpainting model. + # Still takes up a bit of memory, but no encoder call. + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) + + return image_conditioning + + def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 @@ -286,6 +306,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc forced_filename = "" embedding_yet_to_be_embedded = False + img_c = None pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) for i, entries in pbar: embedding.step = i + ititial_step @@ -299,8 +320,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc with torch.autocast("cuda"): c = cond_model([entry.cond_text for entry in entries]) + if img_c is None: + img_c = create_dummy_mask(c, training_width, training_height) + x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] + cond = {"c_concat": [img_c], "c_crossattn": [c]} + loss = shared.sd_model(x, cond)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() -- cgit v1.2.3 From bb832d7725187f8a8ab44faa6ee1b38cb5f600aa Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Sat, 5 Nov 2022 11:48:38 +0700 Subject: Simplify grad clip --- modules/hypernetworks/hypernetwork.py | 16 +++++++--------- modules/textual_inversion/textual_inversion.py | 16 +++++++--------- 2 files changed, 14 insertions(+), 18 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f4c2668f..02b624e1 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -385,10 +385,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - clip_grad_mode_value = clip_grad_mode == "value" - clip_grad_mode_norm = clip_grad_mode == "norm" - clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm - if clip_grad_enabled: + clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \ + torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \ + None + if clip_grad: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this @@ -433,7 +433,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if shared.state.interrupted: break - if clip_grad_enabled: + if clip_grad: clip_grad_sched.step(hypernetwork.step) with torch.autocast("cuda"): @@ -458,10 +458,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log steps_without_grad = 0 assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue' - if clip_grad_mode_value: - torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_sched.learn_rate) - elif clip_grad_mode_norm: - torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_sched.learn_rate) + if clip_grad: + clip_grad(weights, clip_grad_sched.learn_rate) optimizer.step() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c567ec3f..687d97bb 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -269,10 +269,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - clip_grad_mode_value = clip_grad_mode == "value" - clip_grad_mode_norm = clip_grad_mode == "norm" - clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm - if clip_grad_enabled: + clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \ + torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \ + None + if clip_grad: clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." @@ -302,7 +302,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if shared.state.interrupted: break - if clip_grad_enabled: + if clip_grad: clip_grad_sched.step(embedding.step) with torch.autocast("cuda"): @@ -316,10 +316,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc optimizer.zero_grad() loss.backward() - if clip_grad_mode_value: - torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_sched.learn_rate) - elif clip_grad_mode_norm: - torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_sched.learn_rate) + if clip_grad: + clip_grad(embedding.vec, clip_grad_sched.learn_rate) optimizer.step() -- cgit v1.2.3 From f55ac33d446185680604e872ceda2ae858821d5c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 31 Dec 2022 11:27:02 -0500 Subject: validate textual inversion embeddings --- modules/sd_models.py | 3 ++ modules/textual_inversion/textual_inversion.py | 43 +++++++++++++++++++++++--- modules/ui.py | 2 -- 3 files changed, 41 insertions(+), 7 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/sd_models.py b/modules/sd_models.py index ecdd91c5..ebd4dff7 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -325,6 +325,9 @@ def load_model(checkpoint_info=None): script_callbacks.model_loaded_callback(sd_model) print("Model loaded.") + + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload = True) # Reload embeddings after model load as they may or may not fit the model + return sd_model diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f6112578..103ace60 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -23,6 +23,8 @@ class Embedding: self.vec = vec self.name = name self.step = step + self.shape = None + self.vectors = 0 self.cached_checksum = None self.sd_checkpoint = None self.sd_checkpoint_name = None @@ -57,8 +59,10 @@ class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} self.word_embeddings = {} + self.skipped_embeddings = [] self.dir_mtime = None self.embeddings_dir = embeddings_dir + self.expected_shape = -1 def register_embedding(self, embedding, model): @@ -75,14 +79,35 @@ class EmbeddingDatabase: return embedding - def load_textual_inversion_embeddings(self): + def get_expected_shape(self): + expected_shape = -1 # initialize with unknown + idx = torch.tensor(0).to(shared.device) + if expected_shape == -1: + try: # matches sd15 signature + first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx) + expected_shape = first_embedding.shape[0] + except: + pass + if expected_shape == -1: + try: # matches sd20 signature + first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx) + expected_shape = first_embedding.shape[0] + except: + pass + if expected_shape == -1: + print('Could not determine expected embeddings shape from model') + return expected_shape + + def load_textual_inversion_embeddings(self, force_reload = False): mt = os.path.getmtime(self.embeddings_dir) - if self.dir_mtime is not None and mt <= self.dir_mtime: + if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime: return self.dir_mtime = mt self.ids_lookup.clear() self.word_embeddings.clear() + self.skipped_embeddings = [] + self.expected_shape = self.get_expected_shape() def process_file(path, filename): name = os.path.splitext(filename)[0] @@ -122,7 +147,14 @@ class EmbeddingDatabase: embedding.step = data.get('step', None) embedding.sd_checkpoint = data.get('sd_checkpoint', None) embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) - self.register_embedding(embedding, shared.sd_model) + embedding.vectors = vec.shape[0] + embedding.shape = vec.shape[-1] + + if (self.expected_shape == -1) or (self.expected_shape == embedding.shape): + self.register_embedding(embedding, shared.sd_model) + else: + self.skipped_embeddings.append(name) + # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape)) for fn in os.listdir(self.embeddings_dir): try: @@ -137,8 +169,9 @@ class EmbeddingDatabase: print(traceback.format_exc(), file=sys.stderr) continue - print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") - print("Embeddings:", ', '.join(self.word_embeddings.keys())) + print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys()))) + if (len(self.skipped_embeddings) > 0): + print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings))) def find_embedding_at_position(self, tokens, offset): token = tokens[offset] diff --git a/modules/ui.py b/modules/ui.py index 57ee0465..397dd804 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1157,8 +1157,6 @@ def create_ui(): with gr.Column(variant='panel'): submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False) - sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() - with gr.Blocks(analytics_enabled=False) as train_interface: with gr.Row().style(equal_height=False): gr.HTML(value="

See wiki for detailed explanation.

") -- cgit v1.2.3 From bdbe09827b39be63c9c0b3636132ca58da38ebf6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 31 Dec 2022 22:49:09 +0300 Subject: changed embedding accepted shape detection to use existing code and support the new alt-diffusion model, and reformatted messages a bit #6149 --- modules/textual_inversion/textual_inversion.py | 30 ++++++-------------------- 1 file changed, 6 insertions(+), 24 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 103ace60..66f40367 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -80,23 +80,8 @@ class EmbeddingDatabase: return embedding def get_expected_shape(self): - expected_shape = -1 # initialize with unknown - idx = torch.tensor(0).to(shared.device) - if expected_shape == -1: - try: # matches sd15 signature - first_embedding = shared.sd_model.cond_stage_model.wrapped.transformer.text_model.embeddings.token_embedding.wrapped(idx) - expected_shape = first_embedding.shape[0] - except: - pass - if expected_shape == -1: - try: # matches sd20 signature - first_embedding = shared.sd_model.cond_stage_model.wrapped.model.token_embedding.wrapped(idx) - expected_shape = first_embedding.shape[0] - except: - pass - if expected_shape == -1: - print('Could not determine expected embeddings shape from model') - return expected_shape + vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1) + return vec.shape[1] def load_textual_inversion_embeddings(self, force_reload = False): mt = os.path.getmtime(self.embeddings_dir) @@ -112,8 +97,6 @@ class EmbeddingDatabase: def process_file(path, filename): name = os.path.splitext(filename)[0] - data = [] - if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']: embed_image = Image.open(path) if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: @@ -150,11 +133,10 @@ class EmbeddingDatabase: embedding.vectors = vec.shape[0] embedding.shape = vec.shape[-1] - if (self.expected_shape == -1) or (self.expected_shape == embedding.shape): + if self.expected_shape == -1 or self.expected_shape == embedding.shape: self.register_embedding(embedding, shared.sd_model) else: self.skipped_embeddings.append(name) - # print('Skipping embedding {name}: shape was {shape} expected {expected}'.format(name = name, shape = embedding.shape, expected = self.expected_shape)) for fn in os.listdir(self.embeddings_dir): try: @@ -169,9 +151,9 @@ class EmbeddingDatabase: print(traceback.format_exc(), file=sys.stderr) continue - print("Textual inversion embeddings {num} loaded: {val}".format(num = len(self.word_embeddings), val = ', '.join(self.word_embeddings.keys()))) - if (len(self.skipped_embeddings) > 0): - print("Textual inversion embeddings {num} skipped: {val}".format(num = len(self.skipped_embeddings), val = ', '.join(self.skipped_embeddings))) + print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}") + if len(self.skipped_embeddings) > 0: + print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}") def find_embedding_at_position(self, tokens, offset): token = tokens[offset] -- cgit v1.2.3 From 311354c0bb8930ea939d6aa6b3edd50c69301320 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 2 Jan 2023 00:38:09 +0300 Subject: fix the issue with training on SD2.0 --- modules/sd_models.py | 2 ++ modules/textual_inversion/textual_inversion.py | 3 +-- 2 files changed, 3 insertions(+), 2 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/sd_models.py b/modules/sd_models.py index ebd4dff7..bff8d6c9 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info + model.logvar = model.logvar.to(devices.device) # fix for training + sd_vae.delete_base_vae() sd_vae.clear_loaded_vae() vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 66f40367..1e5722e7 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -282,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ return embedding, filename scheduler = LearnRateScheduler(learn_rate, steps, initial_step) - # dataset loading may take a while, so input validations and early returns should be done before this + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed @@ -310,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ loss_step = 0 _loss_step = 0 #internal - last_saved_file = "" last_saved_image = "" forced_filename = "" -- cgit v1.2.3 From c65909ad16a1962129114c6251de092f49479b06 Mon Sep 17 00:00:00 2001 From: Philpax Date: Mon, 2 Jan 2023 12:21:22 +1100 Subject: feat(api): return more data for embeddings --- modules/api/api.py | 17 +++++++++++++++-- modules/api/models.py | 11 +++++++++-- modules/textual_inversion/textual_inversion.py | 8 ++++---- 3 files changed, 28 insertions(+), 8 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/api/api.py b/modules/api/api.py index 30bf3dac..9c670f00 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -330,9 +330,22 @@ class Api: def get_embeddings(self): db = sd_hijack.model_hijack.embedding_db + + def convert_embedding(embedding): + return { + "step": embedding.step, + "sd_checkpoint": embedding.sd_checkpoint, + "sd_checkpoint_name": embedding.sd_checkpoint_name, + "shape": embedding.shape, + "vectors": embedding.vectors, + } + + def convert_embeddings(embeddings): + return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()} + return { - "loaded": sorted(db.word_embeddings.keys()), - "skipped": sorted(db.skipped_embeddings), + "loaded": convert_embeddings(db.word_embeddings), + "skipped": convert_embeddings(db.skipped_embeddings), } def refresh_checkpoints(self): diff --git a/modules/api/models.py b/modules/api/models.py index a8472dc9..4a632c68 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -249,6 +249,13 @@ class ArtistItem(BaseModel): score: float = Field(title="Score") category: str = Field(title="Category") +class EmbeddingItem(BaseModel): + step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") + sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available") + sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead") + shape: int = Field(title="Shape", description="The length of each individual vector in the embedding") + vectors: int = Field(title="Vectors", description="The number of vectors in the embedding") + class EmbeddingsResponse(BaseModel): - loaded: List[str] = Field(title="Loaded", description="Embeddings loaded for the current model") - skipped: List[str] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") \ No newline at end of file + loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") + skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") \ No newline at end of file diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 1e5722e7..fd253477 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -59,7 +59,7 @@ class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} self.word_embeddings = {} - self.skipped_embeddings = [] + self.skipped_embeddings = {} self.dir_mtime = None self.embeddings_dir = embeddings_dir self.expected_shape = -1 @@ -91,7 +91,7 @@ class EmbeddingDatabase: self.dir_mtime = mt self.ids_lookup.clear() self.word_embeddings.clear() - self.skipped_embeddings = [] + self.skipped_embeddings.clear() self.expected_shape = self.get_expected_shape() def process_file(path, filename): @@ -136,7 +136,7 @@ class EmbeddingDatabase: if self.expected_shape == -1 or self.expected_shape == embedding.shape: self.register_embedding(embedding, shared.sd_model) else: - self.skipped_embeddings.append(name) + self.skipped_embeddings[name] = embedding for fn in os.listdir(self.embeddings_dir): try: @@ -153,7 +153,7 @@ class EmbeddingDatabase: print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}") if len(self.skipped_embeddings) > 0: - print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings)}") + print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}") def find_embedding_at_position(self, tokens, offset): token = tokens[offset] -- cgit v1.2.3 From bddebe09edeb6a18f2c06986d5658a7be3a563ea Mon Sep 17 00:00:00 2001 From: Shondoit Date: Tue, 3 Jan 2023 10:26:37 +0100 Subject: Save Optimizer next to TI embedding Also add check to load only .PT and .BIN files as embeddings. (since we add .optim files in the same directory) --- modules/shared.py | 2 +- modules/textual_inversion/textual_inversion.py | 40 ++++++++++++++++++++------ 2 files changed, 33 insertions(+), 9 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/shared.py b/modules/shared.py index 23657a93..c541d18c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -355,7 +355,7 @@ options_templates.update(options_section(('system', "System"), { options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."), - "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."), + "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fd253477..16176e90 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -28,6 +28,7 @@ class Embedding: self.cached_checksum = None self.sd_checkpoint = None self.sd_checkpoint_name = None + self.optimizer_state_dict = None def save(self, filename): embedding_data = { @@ -41,6 +42,13 @@ class Embedding: torch.save(embedding_data, filename) + if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None: + optimizer_saved_dict = { + 'hash': self.checksum(), + 'optimizer_state_dict': self.optimizer_state_dict, + } + torch.save(optimizer_saved_dict, filename + '.optim') + def checksum(self): if self.cached_checksum is not None: return self.cached_checksum @@ -95,9 +103,10 @@ class EmbeddingDatabase: self.expected_shape = self.get_expected_shape() def process_file(path, filename): - name = os.path.splitext(filename)[0] + name, ext = os.path.splitext(filename) + ext = ext.upper() - if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']: + if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']: embed_image = Image.open(path) if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: data = embedding_from_b64(embed_image.text['sd-ti-embedding']) @@ -105,8 +114,10 @@ class EmbeddingDatabase: else: data = extract_image_data_embed(embed_image) name = data.get('name', name) - else: + elif ext in ['.BIN', '.PT']: data = torch.load(path, map_location="cpu") + else: + return # textual inversion embeddings if 'string_to_param' in data: @@ -300,6 +311,20 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ embedding.vec.requires_grad = True optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0) + if shared.opts.save_optimizer_state: + optimizer_state_dict = None + if os.path.exists(filename + '.optim'): + optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu') + if embedding.checksum() == optimizer_saved_dict.get('hash', None): + optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + + if optimizer_state_dict is not None: + optimizer.load_state_dict(optimizer_state_dict) + print("Loaded existing optimizer from checkpoint") + else: + print("No saved optimizer exists in checkpoint") + + scaler = torch.cuda.amp.GradScaler() batch_size = ds.batch_size @@ -366,9 +391,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ # Before saving, change name to match current checkpoint. embedding_name_every = f'{embedding_name}-{steps_done}' last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt') - #if shared.opts.save_optimizer_state: - #embedding.optimizer_state_dict = optimizer.state_dict() - save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True) + save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True) embedding_yet_to_be_embedded = True write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, { @@ -458,7 +481,7 @@ Last saved image: {html.escape(last_saved_image)}

""" filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt') - save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True) + save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True) except Exception: print(traceback.format_exc(), file=sys.stderr) pass @@ -470,7 +493,7 @@ Last saved image: {html.escape(last_saved_image)}
return embedding, filename -def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True): +def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True): old_embedding_name = embedding.name old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None @@ -481,6 +504,7 @@ def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cache if remove_cached_checksum: embedding.cached_checksum = None embedding.name = embedding_name + embedding.optimizer_state_dict = optimizer.state_dict() embedding.save(filename) except: embedding.sd_checkpoint = old_sd_checkpoint -- cgit v1.2.3 From 192ddc04d6de0d780f73aa5fbaa8c66cd4642e1c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 10:34:51 -0500 Subject: add job info to modules --- modules/extras.py | 17 +++++++++++++---- modules/hypernetworks/hypernetwork.py | 1 + modules/textual_inversion/preprocess.py | 1 + modules/textual_inversion/textual_inversion.py | 1 + 4 files changed, 16 insertions(+), 4 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/extras.py b/modules/extras.py index 7e222313..d665440a 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -58,6 +58,9 @@ cached_images: LruCache = LruCache(max_size=5) def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True): devices.torch_gc() + shared.state.begin() + shared.state.job = 'extras' + imageArr = [] # Also keep track of original file names imageNameArr = [] @@ -94,6 +97,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ # Extra operation definitions def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]: + shared.state.job = 'extras-gfpgan' restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) @@ -104,6 +108,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return (res, info) def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]: + shared.state.job = 'extras-codeformer' restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) @@ -114,6 +119,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return (res, info) def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): + shared.state.job = 'extras-upscale' upscaler = shared.sd_upscalers[scaler_index] res = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: @@ -180,6 +186,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ for image, image_name in zip(imageArr, imageNameArr): if image is None: return outputs, "Please select an input image.", '' + + shared.state.textinfo = f'Processing image {image_name}' + existing_pnginfo = image.info or {} image = image.convert("RGB") @@ -193,6 +202,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ else: basename = '' + if opts.enable_pnginfo: # append info before save + image.info = existing_pnginfo + image.info["extras"] = info + if save_output: # Add upscaler name as a suffix. suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else "" @@ -203,10 +216,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix) - if opts.enable_pnginfo: - image.info = existing_pnginfo - image.info["extras"] = info - if extras_mode != 2 or show_extras_results : outputs.append(image) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 109e8078..450fecac 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -417,6 +417,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, shared.loaded_hypernetwork = Hypernetwork() shared.loaded_hypernetwork.load(path) + shared.state.job = "train-hypernetwork" shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 56b9b2eb..feb876c6 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -124,6 +124,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre files = listfiles(src) + shared.state.job = "preprocess" shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fd253477..2c1251d6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -245,6 +245,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ create_image_every = create_image_every or 0 validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding") + shared.state.job = "train-embedding" shared.state.textinfo = "Initializing textual inversion training..." shared.state.job_count = steps -- cgit v1.2.3 From 184e670126f5fc50ba56fa0fedcf0cf60e45ed7e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 17:45:01 +0300 Subject: fix the merge --- modules/textual_inversion/textual_inversion.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5421a758..8731ea5d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -251,6 +251,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat if save_model_every or create_image_every: assert log_directory, "Log directory is empty" + def create_dummy_mask(x, width=None, height=None): if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}: @@ -380,17 +381,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ break with devices.autocast(): - # c = stack_conds(batch.cond).to(devices.device) - # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory) - # print(mask) - # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory) - - - if img_c is None: - img_c = create_dummy_mask(c, training_width, training_height) - x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) c = shared.sd_model.cond_stage_model(batch.cond_text) + + if img_c is None: + img_c = create_dummy_mask(c, training_width, training_height) + cond = {"c_concat": [img_c], "c_crossattn": [c]} loss = shared.sd_model(x, cond)[0] / gradient_step del x -- cgit v1.2.3 From 525cea924562afd676f55470095268a0f6fca59e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 17:58:07 +0300 Subject: use shared function from processing for creating dummy mask when training inpainting model --- modules/processing.py | 39 +++++++++++++------------- modules/textual_inversion/textual_inversion.py | 33 ++++++---------------- 2 files changed, 29 insertions(+), 43 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/processing.py b/modules/processing.py index c03e77e7..c7264aff 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -76,6 +76,24 @@ def apply_overlay(image, paste_loc, index, overlays): return image +def txt2img_image_conditioning(sd_model, x, width, height): + if sd_model.model.conditioning_key not in {'hybrid', 'concat'}: + # Dummy zero conditioning if we're not using inpainting model. + # Still takes up a bit of memory, but no encoder call. + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) + + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) + image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning)) + + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) + + return image_conditioning + + class StableDiffusionProcessing(): """ The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing @@ -139,26 +157,9 @@ class StableDiffusionProcessing(): self.iteration = 0 def txt2img_image_conditioning(self, x, width=None, height=None): - if self.sampler.conditioning_key not in {'hybrid', 'concat'}: - # Dummy zero conditioning if we're not using inpainting model. - # Still takes up a bit of memory, but no encoder call. - # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. - return x.new_zeros(x.shape[0], 5, 1, 1) + self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'} - self.is_using_inpainting_conditioning = True - - height = height or self.height - width = width or self.width - - # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) - image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) - - # Add the fake full 1s mask to the first dimension. - image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) - image_conditioning = image_conditioning.to(x.dtype) - - return image_conditioning + return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height) def depth2img_image_conditioning(self, source_image): # Use the AddMiDaS helper to Format our source image to suit the MiDaS model diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 8731ea5d..2250e41b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -252,26 +252,6 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat assert log_directory, "Log directory is empty" -def create_dummy_mask(x, width=None, height=None): - if shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}: - - # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) - image_conditioning = shared.sd_model.get_first_stage_encoding(shared.sd_model.encode_first_stage(image_conditioning)) - - # Add the fake full 1s mask to the first dimension. - image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) - image_conditioning = image_conditioning.to(x.dtype) - - else: - # Dummy zero conditioning if we're not using inpainting model. - # Still takes up a bit of memory, but no encoder call. - # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. - image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) - - return image_conditioning - - def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 @@ -346,7 +326,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ else: print("No saved optimizer exists in checkpoint") - scaler = torch.cuda.amp.GradScaler() batch_size = ds.batch_size @@ -362,7 +341,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ forced_filename = "" embedding_yet_to_be_embedded = False + is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'} img_c = None + pbar = tqdm.tqdm(total=steps - initial_step) try: for i in range((steps-initial_step) * gradient_step): @@ -384,10 +365,14 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) c = shared.sd_model.cond_stage_model(batch.cond_text) - if img_c is None: - img_c = create_dummy_mask(c, training_width, training_height) + if is_training_inpainting_model: + if img_c is None: + img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height) + + cond = {"c_concat": [img_c], "c_crossattn": [c]} + else: + cond = c - cond = {"c_concat": [img_c], "c_crossattn": [c]} loss = shared.sd_model(x, cond)[0] / gradient_step del x -- cgit v1.2.3 From eea8fc40e16664ddc8a9aec77206da704a35dde0 Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 5 Jan 2023 07:24:22 -0800 Subject: Add option to save ti settings to file. --- modules/shared.py | 1 + modules/textual_inversion/textual_inversion.py | 30 +++++++++++++++++++++++--- 2 files changed, 28 insertions(+), 3 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/shared.py b/modules/shared.py index e0f44c6d..933cd738 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -362,6 +362,7 @@ options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."), "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."), + "save_train_settings_to_txt": OptionInfo(False, "Save textual inversion and hypernet settings to a text file when training starts."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 71e07bcc..2bed2ecb 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -1,6 +1,7 @@ import os import sys import traceback +import inspect import torch import tqdm @@ -229,6 +230,28 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +def save_settings_to_file(initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + checkpoint = sd_models.select_checkpoint() + model_name = checkpoint.model_name + model_hash = '[{}]'.format(checkpoint.hash) + + # Get a list of the argument names. + arg_names = inspect.getfullargspec(save_settings_to_file).args + + # Create a list of the argument names to include in the settings string. + names = arg_names[:16] # Include all arguments up until the preview-related ones. + if preview_from_txt2img: + names.extend(arg_names[16:]) # Include all remaining arguments if `preview_from_txt2img` is True. + + # Build the settings string. + settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" + for name in names: + value = locals()[name] + settings_str += f"{name}: {value}\n" + + with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: + fout.write(settings_str + "\n\n") + def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"): assert model_name, f"{name} not selected" assert learn_rate, "Learning rate is empty or 0" @@ -292,13 +315,13 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ if initial_step >= steps: shared.state.textinfo = "Model has already been trained beyond specified max steps" return embedding, filename + scheduler = LearnRateScheduler(learn_rate, steps, initial_step) - clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \ torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \ None if clip_grad: - clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) + clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed @@ -306,7 +329,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ pin_memory = shared.opts.pin_memory ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) - + if shared.opts.save_train_settings_to_txt: + save_settings_to_file(initial_step , len(ds) , embedding_name, len(embedding.vec) , learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) latent_sampling_method = ds.latent_sampling_method dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) -- cgit v1.2.3 From b85c2b5cf4a6809bc871718cf4680d49c3e95e94 Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 5 Jan 2023 08:14:38 -0800 Subject: Clean up ti, add same behavior to hypernetwork. --- modules/hypernetworks/hypernetwork.py | 31 +++++++++++++++++++++++++- modules/shared.py | 2 +- modules/textual_inversion/textual_inversion.py | 14 +++++++----- 3 files changed, 40 insertions(+), 7 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6a9b1398..d5985263 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -401,7 +401,33 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, hypernet.save(fn) shared.reload_hypernetworks() +# Note: textual_inversion.py has a nearly identical function of the same name. +def save_settings_to_file(initial_step, num_of_dataset_images, hypernetwork_name, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + checkpoint = sd_models.select_checkpoint() + model_name = checkpoint.model_name + model_hash = '[{}]'.format(checkpoint.hash) + # Starting index of preview-related arguments. + border_index = 19 + + # Get a list of the argument names, excluding default argument. + sig = inspect.signature(save_settings_to_file) + arg_names = [p.name for p in sig.parameters.values() if p.default == p.empty] + + # Create a list of the argument names to include in the settings string. + names = arg_names[:border_index] # Include all arguments up until the preview-related ones. + + # Include preview-related arguments if applicable. + if preview_from_txt2img: + names.extend(arg_names[border_index:]) + + # Build the settings string. + settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" + for name in names: + value = locals()[name] + settings_str += f"{name}: {value}\n" + with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: + fout.write(settings_str + "\n\n") def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. @@ -457,7 +483,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, pin_memory = shared.opts.pin_memory ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) - + + if shared.opts.save_training_settings_to_txt: + save_settings_to_file(initial_step, len(ds), hypernetwork_name, hypernetwork.layer_structure, hypernetwork.activation_func, hypernetwork.weight_init, hypernetwork.add_layer_norm, hypernetwork.use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + latent_sampling_method = ds.latent_sampling_method dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) diff --git a/modules/shared.py b/modules/shared.py index 933cd738..10231a75 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -362,7 +362,7 @@ options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."), "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."), - "save_train_settings_to_txt": OptionInfo(False, "Save textual inversion and hypernet settings to a text file when training starts."), + "save_training_settings_to_txt": OptionInfo(False, "Save textual inversion and hypernet settings to a text file whenever training starts."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 2bed2ecb..68648550 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -230,18 +230,20 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) +# Note: hypernetwork.py has a nearly identical function of the same name. def save_settings_to_file(initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): checkpoint = sd_models.select_checkpoint() model_name = checkpoint.model_name model_hash = '[{}]'.format(checkpoint.hash) - + # Starting index of preview-related arguments. + border_index = 16 # Get a list of the argument names. arg_names = inspect.getfullargspec(save_settings_to_file).args # Create a list of the argument names to include in the settings string. - names = arg_names[:16] # Include all arguments up until the preview-related ones. + names = arg_names[:border_index] # Include all arguments up until the preview-related ones. if preview_from_txt2img: - names.extend(arg_names[16:]) # Include all remaining arguments if `preview_from_txt2img` is True. + names.extend(arg_names[border_index:]) # Include all remaining arguments if `preview_from_txt2img` is True. # Build the settings string. settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" @@ -329,8 +331,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ pin_memory = shared.opts.pin_memory ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) - if shared.opts.save_train_settings_to_txt: - save_settings_to_file(initial_step , len(ds) , embedding_name, len(embedding.vec) , learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + + if shared.opts.save_training_settings_to_txt: + save_settings_to_file(initial_step, len(ds), embedding_name, len(embedding.vec), learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + latent_sampling_method = ds.latent_sampling_method dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) -- cgit v1.2.3 From b6bab2f052b32c0ffebe6aecc1819ccf20cf8c5d Mon Sep 17 00:00:00 2001 From: timntorres Date: Thu, 5 Jan 2023 09:14:56 -0800 Subject: Include model in log file. Exclude directory. --- modules/hypernetworks/hypernetwork.py | 28 +++++++++----------------- modules/textual_inversion/textual_inversion.py | 22 +++++++++----------- 2 files changed, 19 insertions(+), 31 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index d5985263..3237c37a 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -402,30 +402,22 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() # Note: textual_inversion.py has a nearly identical function of the same name. -def save_settings_to_file(initial_step, num_of_dataset_images, hypernetwork_name, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - checkpoint = sd_models.select_checkpoint() - model_name = checkpoint.model_name - model_hash = '[{}]'.format(checkpoint.hash) +def save_settings_to_file(model_name, model_hash, initial_step, num_of_dataset_images, hypernetwork_name, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # Starting index of preview-related arguments. - border_index = 19 - - # Get a list of the argument names, excluding default argument. - sig = inspect.signature(save_settings_to_file) - arg_names = [p.name for p in sig.parameters.values() if p.default == p.empty] - + border_index = 21 + # Get a list of the argument names. + arg_names = inspect.getfullargspec(save_settings_to_file).args # Create a list of the argument names to include in the settings string. names = arg_names[:border_index] # Include all arguments up until the preview-related ones. - - # Include preview-related arguments if applicable. if preview_from_txt2img: - names.extend(arg_names[border_index:]) - + names.extend(arg_names[border_index:]) # Include preview-related arguments if applicable. # Build the settings string. settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" for name in names: - value = locals()[name] - settings_str += f"{name}: {value}\n" - + if name != 'log_directory': # It's useless and redundant to save log_directory. + value = locals()[name] + settings_str += f"{name}: {value}\n" + # Create or append to the file. with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: fout.write(settings_str + "\n\n") @@ -485,7 +477,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) if shared.opts.save_training_settings_to_txt: - save_settings_to_file(initial_step, len(ds), hypernetwork_name, hypernetwork.layer_structure, hypernetwork.activation_func, hypernetwork.weight_init, hypernetwork.add_layer_norm, hypernetwork.use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + save_settings_to_file(checkpoint.model_name, '[{}]'.format(checkpoint.hash), initial_step, len(ds), hypernetwork_name, hypernetwork.layer_structure, hypernetwork.activation_func, hypernetwork.weight_init, hypernetwork.add_layer_norm, hypernetwork.use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) latent_sampling_method = ds.latent_sampling_method diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 68648550..ce7e4f5d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -231,26 +231,22 @@ def write_loss(log_directory, filename, step, epoch_len, values): }) # Note: hypernetwork.py has a nearly identical function of the same name. -def save_settings_to_file(initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - checkpoint = sd_models.select_checkpoint() - model_name = checkpoint.model_name - model_hash = '[{}]'.format(checkpoint.hash) +def save_settings_to_file(model_name, model_hash, initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # Starting index of preview-related arguments. - border_index = 16 + border_index = 18 # Get a list of the argument names. - arg_names = inspect.getfullargspec(save_settings_to_file).args - + arg_names = inspect.getfullargspec(save_settings_to_file).args # Create a list of the argument names to include in the settings string. names = arg_names[:border_index] # Include all arguments up until the preview-related ones. if preview_from_txt2img: - names.extend(arg_names[border_index:]) # Include all remaining arguments if `preview_from_txt2img` is True. - + names.extend(arg_names[border_index:]) # Include preview-related arguments if applicable. # Build the settings string. settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" for name in names: - value = locals()[name] - settings_str += f"{name}: {value}\n" - + if name != 'log_directory': # It's useless and redundant to save log_directory. + value = locals()[name] + settings_str += f"{name}: {value}\n" + # Create or append to the file. with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: fout.write(settings_str + "\n\n") @@ -333,7 +329,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) if shared.opts.save_training_settings_to_txt: - save_settings_to_file(initial_step, len(ds), embedding_name, len(embedding.vec), learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + save_settings_to_file(checkpoint.model_name, '[{}]'.format(checkpoint.hash), initial_step, len(ds), embedding_name, len(embedding.vec), learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) latent_sampling_method = ds.latent_sampling_method -- cgit v1.2.3 From fda04e620d529031e2134520e74756d0efa30464 Mon Sep 17 00:00:00 2001 From: Kuma <36082288+KumiIT@users.noreply.github.com> Date: Thu, 5 Jan 2023 18:44:19 +0100 Subject: typo in TI --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 71e07bcc..24b43045 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -298,7 +298,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \ None if clip_grad: - clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False) + clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed -- cgit v1.2.3 From 81133d4168ae0bae9bf8bf1a1d4983319a589112 Mon Sep 17 00:00:00 2001 From: Faber Date: Fri, 6 Jan 2023 03:38:37 +0700 Subject: allow loading embeddings from subdirectories --- modules/textual_inversion/textual_inversion.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 24b43045..0a059044 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -149,19 +149,20 @@ class EmbeddingDatabase: else: self.skipped_embeddings[name] = embedding - for fn in os.listdir(self.embeddings_dir): - try: - fullfn = os.path.join(self.embeddings_dir, fn) - - if os.stat(fullfn).st_size == 0: + for root, dirs, fns in os.walk(self.embeddings_dir): + for fn in fns: + try: + fullfn = os.path.join(root, fn) + + if os.stat(fullfn).st_size == 0: + continue + + process_file(fullfn, fn) + except Exception: + print(f"Error loading embedding {fn}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) continue - process_file(fullfn, fn) - except Exception: - print(f"Error loading embedding {fn}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - continue - print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}") if len(self.skipped_embeddings) > 0: print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}") -- cgit v1.2.3 From 683287d87f6401083a8d63eedc00ca7410214ca1 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 6 Jan 2023 08:52:06 +0300 Subject: rework saving training params to file #6372 --- modules/hypernetworks/hypernetwork.py | 28 +++++++------------------- modules/shared.py | 2 +- modules/textual_inversion/logging.py | 24 ++++++++++++++++++++++ modules/textual_inversion/textual_inversion.py | 23 +++------------------ 4 files changed, 35 insertions(+), 42 deletions(-) create mode 100644 modules/textual_inversion/logging.py (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3237c37a..b0cfbe71 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -13,7 +13,7 @@ import tqdm from einops import rearrange, repeat from ldm.util import default from modules import devices, processing, sd_models, shared, sd_samplers -from modules.textual_inversion import textual_inversion +from modules.textual_inversion import textual_inversion, logging from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_ @@ -401,25 +401,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, hypernet.save(fn) shared.reload_hypernetworks() -# Note: textual_inversion.py has a nearly identical function of the same name. -def save_settings_to_file(model_name, model_hash, initial_step, num_of_dataset_images, hypernetwork_name, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - # Starting index of preview-related arguments. - border_index = 21 - # Get a list of the argument names. - arg_names = inspect.getfullargspec(save_settings_to_file).args - # Create a list of the argument names to include in the settings string. - names = arg_names[:border_index] # Include all arguments up until the preview-related ones. - if preview_from_txt2img: - names.extend(arg_names[border_index:]) # Include preview-related arguments if applicable. - # Build the settings string. - settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" - for name in names: - if name != 'log_directory': # It's useless and redundant to save log_directory. - value = locals()[name] - settings_str += f"{name}: {value}\n" - # Create or append to the file. - with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: - fout.write(settings_str + "\n\n") + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. @@ -477,7 +459,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) if shared.opts.save_training_settings_to_txt: - save_settings_to_file(checkpoint.model_name, '[{}]'.format(checkpoint.hash), initial_step, len(ds), hypernetwork_name, hypernetwork.layer_structure, hypernetwork.activation_func, hypernetwork.weight_init, hypernetwork.add_layer_norm, hypernetwork.use_dropout, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + saved_params = dict( + model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), + **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]} + ) + logging.save_settings_to_file(log_directory, {**saved_params, **locals()}) latent_sampling_method = ds.latent_sampling_method diff --git a/modules/shared.py b/modules/shared.py index f0e10b35..57e489d0 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -362,7 +362,7 @@ options_templates.update(options_section(('training', "Training"), { "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."), "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."), "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."), - "save_training_settings_to_txt": OptionInfo(False, "Save textual inversion and hypernet settings to a text file whenever training starts."), + "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), diff --git a/modules/textual_inversion/logging.py b/modules/textual_inversion/logging.py new file mode 100644 index 00000000..8b1981d5 --- /dev/null +++ b/modules/textual_inversion/logging.py @@ -0,0 +1,24 @@ +import datetime +import json +import os + +saved_params_shared = {"model_name", "model_hash", "initial_step", "num_of_dataset_images", "learn_rate", "batch_size", "data_root", "log_directory", "training_width", "training_height", "steps", "create_image_every", "template_file"} +saved_params_ti = {"embedding_name", "num_vectors_per_token", "save_embedding_every", "save_image_with_stored_embedding"} +saved_params_hypernet = {"hypernetwork_name", "layer_structure", "activation_func", "weight_init", "add_layer_norm", "use_dropout", "save_hypernetwork_every"} +saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet +saved_params_previews = {"preview_prompt", "preview_negative_prompt", "preview_steps", "preview_sampler_index", "preview_cfg_scale", "preview_seed", "preview_width", "preview_height"} + + +def save_settings_to_file(log_directory, all_params): + now = datetime.datetime.now() + params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")} + + keys = saved_params_all + if all_params.get('preview_from_txt2img'): + keys = keys | saved_params_previews + + params.update({k: v for k, v in all_params.items() if k in keys}) + + filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json' + with open(os.path.join(log_directory, filename), "w") as file: + json.dump(params, file, indent=4) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e9cf432f..f9f5e8cd 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay) +from modules.textual_inversion.logging import save_settings_to_file + class Embedding: def __init__(self, vec, name, step=None): @@ -231,25 +233,6 @@ def write_loss(log_directory, filename, step, epoch_len, values): **values, }) -# Note: hypernetwork.py has a nearly identical function of the same name. -def save_settings_to_file(model_name, model_hash, initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - # Starting index of preview-related arguments. - border_index = 18 - # Get a list of the argument names. - arg_names = inspect.getfullargspec(save_settings_to_file).args - # Create a list of the argument names to include in the settings string. - names = arg_names[:border_index] # Include all arguments up until the preview-related ones. - if preview_from_txt2img: - names.extend(arg_names[border_index:]) # Include preview-related arguments if applicable. - # Build the settings string. - settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n" - for name in names: - if name != 'log_directory': # It's useless and redundant to save log_directory. - value = locals()[name] - settings_str += f"{name}: {value}\n" - # Create or append to the file. - with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout: - fout.write(settings_str + "\n\n") def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"): assert model_name, f"{name} not selected" @@ -330,7 +313,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) if shared.opts.save_training_settings_to_txt: - save_settings_to_file(checkpoint.model_name, '[{}]'.format(checkpoint.hash), initial_step, len(ds), embedding_name, len(embedding.vec), learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height) + save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()}) latent_sampling_method = ds.latent_sampling_method -- cgit v1.2.3 From 79e39fae6110c20a3ee6255e2841c877f65e8cbd Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 7 Jan 2023 01:45:28 +0300 Subject: CLIP hijack rework --- modules/sd_hijack.py | 6 +- modules/sd_hijack_clip.py | 348 ++++++++++++------------- modules/sd_hijack_clip_old.py | 81 ++++++ modules/textual_inversion/textual_inversion.py | 1 - modules/ui.py | 2 +- 5 files changed, 256 insertions(+), 182 deletions(-) create mode 100644 modules/sd_hijack_clip_old.py (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index fa2cd4bb..71cc145a 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -150,10 +150,10 @@ class StableDiffusionModelHijack: def clear_comments(self): self.comments = [] - def tokenize(self, text): - _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) + def get_prompt_lengths(self, text): + _, token_count = self.clip.process_texts([text]) - return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count) + return token_count, self.clip.get_target_prompt_token_count(token_count) class EmbeddingsWithFixes(torch.nn.Module): diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index ca92b142..ac3020d7 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -1,12 +1,28 @@ import math +from collections import namedtuple import torch from modules import prompt_parser, devices from modules.shared import opts -def get_target_prompt_token_count(token_count): - return math.ceil(max(token_count, 1) / 75) * 75 + +class PromptChunk: + """ + This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt. + If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary. + Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token, + so just 75 tokens from prompt. + """ + + def __init__(self): + self.tokens = [] + self.multipliers = [] + self.fixes = [] + + +PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding']) +"""This is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt chunk""" class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): @@ -14,17 +30,49 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): super().__init__() self.wrapped = wrapped self.hijack = hijack + self.chunk_length = 75 + + def empty_chunk(self): + """creates an empty PromptChunk and returns it""" + + chunk = PromptChunk() + chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1) + chunk.multipliers = [1.0] * (self.chunk_length + 2) + return chunk + + def get_target_prompt_token_count(self, token_count): + """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented""" + + return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length def tokenize(self, texts): + """Converts a batch of texts into a batch of token ids""" + raise NotImplementedError def encode_with_transformers(self, tokens): + """ + converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens; + All python lists with tokens are assumed to have same length, usually 77. + if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on + model - can be 768 and 1024 + """ + raise NotImplementedError def encode_embedding_init_text(self, init_text, nvpt): + """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through + transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned.""" + raise NotImplementedError - def tokenize_line(self, line, used_custom_terms, hijack_comments): + def tokenize_line(self, line): + """ + this transforms a single prompt into a list of PromptChunk objects - as many as needed to + represent the prompt. + Returns the list and the total number of tokens in the prompt. + """ + if opts.enable_emphasis: parsed = prompt_parser.parse_prompt_attention(line) else: @@ -32,205 +80,152 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): tokenized = self.tokenize([text for text, _ in parsed]) - fixes = [] - remade_tokens = [] - multipliers = [] + chunks = [] + chunk = PromptChunk() + token_count = 0 last_comma = -1 - for tokens, (text, weight) in zip(tokenized, parsed): - i = 0 - while i < len(tokens): - token = tokens[i] + def next_chunk(): + """puts current chunk into the list of results and produces the next one - empty""" + nonlocal token_count + nonlocal last_comma + nonlocal chunk + + token_count += len(chunk.tokens) + to_add = self.chunk_length - len(chunk.tokens) + if to_add > 0: + chunk.tokens += [self.id_end] * to_add + chunk.multipliers += [1.0] * to_add - embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end] + chunk.multipliers = [1.0] + chunk.multipliers + [1.0] + + last_comma = -1 + chunks.append(chunk) + chunk = PromptChunk() + + for tokens, (text, weight) in zip(tokenized, parsed): + position = 0 + while position < len(tokens): + token = tokens[position] if token == self.comma_token: - last_comma = len(remade_tokens) - elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack: - last_comma += 1 - reloc_tokens = remade_tokens[last_comma:] - reloc_mults = multipliers[last_comma:] + last_comma = len(chunk.tokens) + + # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack + # is a setting that specifies that is there is a comma nearby, the text after comma should be moved out of this chunk and into the next. + elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack: + break_location = last_comma + 1 + + reloc_tokens = chunk.tokens[break_location:] + reloc_mults = chunk.multipliers[break_location:] - remade_tokens = remade_tokens[:last_comma] - length = len(remade_tokens) + chunk.tokens = chunk.tokens[:break_location] + chunk.multipliers = chunk.multipliers[:break_location] - rem = int(math.ceil(length / 75)) * 75 - length - remade_tokens += [self.id_end] * rem + reloc_tokens - multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults + next_chunk() + chunk.tokens = reloc_tokens + chunk.multipliers = reloc_mults + if len(chunk.tokens) == self.chunk_length: + next_chunk() + + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position) if embedding is None: - remade_tokens.append(token) - multipliers.append(weight) - i += 1 - else: - emb_len = int(embedding.vec.shape[0]) - iteration = len(remade_tokens) // 75 - if (len(remade_tokens) + emb_len) // 75 != iteration: - rem = (75 * (iteration + 1) - len(remade_tokens)) - remade_tokens += [self.id_end] * rem - multipliers += [1.0] * rem - iteration += 1 - fixes.append((iteration, (len(remade_tokens) % 75, embedding))) - remade_tokens += [0] * emb_len - multipliers += [weight] * emb_len - used_custom_terms.append((embedding.name, embedding.checksum())) - i += embedding_length_in_tokens - - token_count = len(remade_tokens) - prompt_target_length = get_target_prompt_token_count(token_count) - tokens_to_add = prompt_target_length - len(remade_tokens) - - remade_tokens = remade_tokens + [self.id_end] * tokens_to_add - multipliers = multipliers + [1.0] * tokens_to_add - - return remade_tokens, fixes, multipliers, token_count - - def process_text(self, texts): - used_custom_terms = [] - remade_batch_tokens = [] - hijack_comments = [] - hijack_fixes = [] + chunk.tokens.append(token) + chunk.multipliers.append(weight) + position += 1 + continue + + emb_len = int(embedding.vec.shape[0]) + if len(chunk.tokens) + emb_len > self.chunk_length: + next_chunk() + + chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding)) + + chunk.tokens += [0] * emb_len + chunk.multipliers += [weight] * emb_len + position += embedding_length_in_tokens + + if len(chunk.tokens) > 0: + next_chunk() + + return chunks, token_count + + def process_texts(self, texts): + """ + Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum + length, in tokens, of all texts. + """ + token_count = 0 cache = {} - batch_multipliers = [] + batch_chunks = [] for line in texts: if line in cache: - remade_tokens, fixes, multipliers = cache[line] + chunks = cache[line] else: - remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) + chunks, current_token_count = self.tokenize_line(line) token_count = max(current_token_count, token_count) - cache[line] = (remade_tokens, fixes, multipliers) + cache[line] = chunks - remade_batch_tokens.append(remade_tokens) - hijack_fixes.append(fixes) - batch_multipliers.append(multipliers) + batch_chunks.append(chunks) - return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count + return batch_chunks, token_count - def process_text_old(self, texts): - id_start = self.id_start - id_end = self.id_end - maxlen = self.wrapped.max_length # you get to stay at 77 - used_custom_terms = [] - remade_batch_tokens = [] - hijack_comments = [] - hijack_fixes = [] - token_count = 0 + def forward(self, texts): + """ + Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts. + Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will + be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, and for SD2 it's 1024. + An example shape returned by this function can be: (2, 77, 768). + Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet + is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream" + """ - cache = {} - batch_tokens = self.tokenize(texts) - batch_multipliers = [] - for tokens in batch_tokens: - tuple_tokens = tuple(tokens) + if opts.use_old_emphasis_implementation: + import modules.sd_hijack_clip_old + return modules.sd_hijack_clip_old.forward_old(self, texts) - if tuple_tokens in cache: - remade_tokens, fixes, multipliers = cache[tuple_tokens] - else: - fixes = [] - remade_tokens = [] - multipliers = [] - mult = 1.0 - - i = 0 - while i < len(tokens): - token = tokens[i] - - embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) - - mult_change = self.token_mults.get(token) if opts.enable_emphasis else None - if mult_change is not None: - mult *= mult_change - i += 1 - elif embedding is None: - remade_tokens.append(token) - multipliers.append(mult) - i += 1 - else: - emb_len = int(embedding.vec.shape[0]) - fixes.append((len(remade_tokens), embedding)) - remade_tokens += [0] * emb_len - multipliers += [mult] * emb_len - used_custom_terms.append((embedding.name, embedding.checksum())) - i += embedding_length_in_tokens - - if len(remade_tokens) > maxlen - 2: - vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} - ovf = remade_tokens[maxlen - 2:] - overflowing_words = [vocab.get(int(x), "") for x in ovf] - overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) - hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") - - token_count = len(remade_tokens) - remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) - remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end] - cache[tuple_tokens] = (remade_tokens, fixes, multipliers) - - multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) - multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] - - remade_batch_tokens.append(remade_tokens) - hijack_fixes.append(fixes) - batch_multipliers.append(multipliers) - return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - - def forward(self, text): - use_old = opts.use_old_emphasis_implementation - if use_old: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) - else: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) - - self.hijack.comments += hijack_comments - - if len(used_custom_terms) > 0: - self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) - - if use_old: - self.hijack.fixes = hijack_fixes - return self.process_tokens(remade_batch_tokens, batch_multipliers) - - z = None - i = 0 - while max(map(len, remade_batch_tokens)) != 0: - rem_tokens = [x[75:] for x in remade_batch_tokens] - rem_multipliers = [x[75:] for x in batch_multipliers] - - self.hijack.fixes = [] - for unfiltered in hijack_fixes: - fixes = [] - for fix in unfiltered: - if fix[0] == i: - fixes.append(fix[1]) - self.hijack.fixes.append(fixes) - - tokens = [] - multipliers = [] - for j in range(len(remade_batch_tokens)): - if len(remade_batch_tokens[j]) > 0: - tokens.append(remade_batch_tokens[j][:75]) - multipliers.append(batch_multipliers[j][:75]) - else: - tokens.append([self.id_end] * 75) - multipliers.append([1.0] * 75) - - z1 = self.process_tokens(tokens, multipliers) - z = z1 if z is None else torch.cat((z, z1), axis=-2) - - remade_batch_tokens = rem_tokens - batch_multipliers = rem_multipliers - i += 1 + batch_chunks, token_count = self.process_texts(texts) - return z + used_embeddings = {} + chunk_count = max([len(x) for x in batch_chunks]) - def process_tokens(self, remade_batch_tokens, batch_multipliers): - if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens] - batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] + zs = [] + for i in range(chunk_count): + batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks] + + tokens = [x.tokens for x in batch_chunk] + multipliers = [x.multipliers for x in batch_chunk] + self.hijack.fixes = [x.fixes for x in batch_chunk] + for fixes in self.hijack.fixes: + for position, embedding in fixes: + used_embeddings[embedding.name] = embedding + + z = self.process_tokens(tokens, multipliers) + zs.append(z) + + if len(used_embeddings) > 0: + embeddings_list = ", ".join([f'{name} [{embedding.checksum()}]' for name, embedding in used_embeddings.items()]) + self.hijack.comments.append(f"Used embeddings: {embeddings_list}") + + return torch.hstack(zs) + + def process_tokens(self, remade_batch_tokens, batch_multipliers): + """ + sends one single prompt chunk to be encoded by transformers neural network. + remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually + there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens. + Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier + corresponds to one token. + """ tokens = torch.asarray(remade_batch_tokens).to(devices.device) + # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones. if self.id_end != self.id_pad: for batch_pos in range(len(remade_batch_tokens)): index = remade_batch_tokens[batch_pos].index(self.id_end) @@ -239,8 +234,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): z = self.encode_with_transformers(tokens) # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise - batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers] - batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device) + batch_multipliers = torch.asarray(batch_multipliers).to(devices.device) original_mean = z.mean() z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape) new_mean = z.mean() diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py new file mode 100644 index 00000000..6d9fbbe6 --- /dev/null +++ b/modules/sd_hijack_clip_old.py @@ -0,0 +1,81 @@ +from modules import sd_hijack_clip +from modules import shared + + +def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts): + id_start = self.id_start + id_end = self.id_end + maxlen = self.wrapped.max_length # you get to stay at 77 + used_custom_terms = [] + remade_batch_tokens = [] + hijack_comments = [] + hijack_fixes = [] + token_count = 0 + + cache = {} + batch_tokens = self.tokenize(texts) + batch_multipliers = [] + for tokens in batch_tokens: + tuple_tokens = tuple(tokens) + + if tuple_tokens in cache: + remade_tokens, fixes, multipliers = cache[tuple_tokens] + else: + fixes = [] + remade_tokens = [] + multipliers = [] + mult = 1.0 + + i = 0 + while i < len(tokens): + token = tokens[i] + + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + + mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None + if mult_change is not None: + mult *= mult_change + i += 1 + elif embedding is None: + remade_tokens.append(token) + multipliers.append(mult) + i += 1 + else: + emb_len = int(embedding.vec.shape[0]) + fixes.append((len(remade_tokens), embedding)) + remade_tokens += [0] * emb_len + multipliers += [mult] * emb_len + used_custom_terms.append((embedding.name, embedding.checksum())) + i += embedding_length_in_tokens + + if len(remade_tokens) > maxlen - 2: + vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()} + ovf = remade_tokens[maxlen - 2:] + overflowing_words = [vocab.get(int(x), "") for x in ovf] + overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) + hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") + + token_count = len(remade_tokens) + remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) + remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end] + cache[tuple_tokens] = (remade_tokens, fixes, multipliers) + + multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) + multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0] + + remade_batch_tokens.append(remade_tokens) + hijack_fixes.append(fixes) + batch_multipliers.append(multipliers) + return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count + + +def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts): + batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts) + + self.hijack.comments += hijack_comments + + if len(used_custom_terms) > 0: + self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + + self.hijack.fixes = hijack_fixes + return self.process_tokens(remade_batch_tokens, batch_multipliers) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f9f5e8cd..45882ed6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -79,7 +79,6 @@ class EmbeddingDatabase: self.word_embeddings[embedding.name] = embedding - # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working ids = model.cond_stage_model.tokenize([embedding.name])[0] first_id = ids[0] diff --git a/modules/ui.py b/modules/ui.py index b79d24ee..5d2f5bad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -368,7 +368,7 @@ def update_token_counter(text, steps): flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules) prompts = [prompt_text for step, prompt_text in flat_prompts] - tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1]) + token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0]) style_class = ' class="red"' if (token_count > max_length) else "" return f"{token_count}/{max_length}" -- cgit v1.2.3 From 448b9cedab66e05b5b2800513ca334a769b42aa7 Mon Sep 17 00:00:00 2001 From: dan Date: Sat, 7 Jan 2023 21:07:27 +0800 Subject: Allow variable img size --- modules/textual_inversion/dataset.py | 18 +++++++++++------- modules/textual_inversion/textual_inversion.py | 4 ++-- 2 files changed, 13 insertions(+), 9 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 88d68c76..375178ed 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*") class DatasetEntry: - def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None): + def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, img_shape=None): self.filename = filename self.filename_text = filename_text self.latent_dist = latent_dist @@ -25,6 +25,7 @@ class DatasetEntry: self.cond = cond self.cond_text = cond_text self.pixel_values = pixel_values + self.img_shape = img_shape class PersonalizedBase(Dataset): @@ -33,8 +34,6 @@ class PersonalizedBase(Dataset): self.placeholder_token = placeholder_token - self.width = width - self.height = height self.flip = transforms.RandomHorizontalFlip(p=flip_p) self.dataset = [] @@ -59,7 +58,11 @@ class PersonalizedBase(Dataset): if shared.state.interrupted: raise Exception("interrupted") try: - image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) + image = Image.open(path).convert('RGB') + if width < 2000: + image = image.resize((width, height), PIL.Image.BICUBIC) + else: + assert batch_size == 1, 'variable img size must have batch size 1' except Exception: continue @@ -88,14 +91,14 @@ class PersonalizedBase(Dataset): if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)): latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) latent_sampling_method = "once" - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size) elif latent_sampling_method == "deterministic": # Works only for DiagonalGaussianDistribution latent_dist.std = 0 latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size) elif latent_sampling_method == "random": - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size) if not (self.tag_drop_out != 0 or self.shuffle_tags): entry.cond_text = self.create_text(filename_text) @@ -151,6 +154,7 @@ class BatchLoader: self.cond_text = [entry.cond_text for entry in data] self.cond = [entry.cond for entry in data] self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1) + self.img_shape = [entry.img_shape for entry in data] #self.emb_index = [entry.emb_index for entry in data] #print(self.latent_sample.device) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 45882ed6..9f96d0fd 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -451,8 +451,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ else: p.prompt = batch.cond_text[0] p.steps = 20 - p.width = training_width - p.height = training_height + p.width = batch.img_shape[0][0] + p.height = batch.img_shape[0][1] preview_text = p.prompt -- cgit v1.2.3 From 669fb18d5222f53ae48abe0f30393d846c50ad91 Mon Sep 17 00:00:00 2001 From: dan Date: Sun, 8 Jan 2023 01:34:52 +0800 Subject: Add checkbox for variable training dims --- modules/hypernetworks/hypernetwork.py | 2 +- modules/textual_inversion/dataset.py | 4 ++-- modules/textual_inversion/textual_inversion.py | 4 ++-- modules/ui.py | 3 +++ 4 files changed, 8 insertions(+), 5 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b0cfbe71..dba52841 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -403,7 +403,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 375178ed..7f8a314f 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -29,7 +29,7 @@ class DatasetEntry: class PersonalizedBase(Dataset): - def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False): re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None self.placeholder_token = placeholder_token @@ -59,7 +59,7 @@ class PersonalizedBase(Dataset): raise Exception("interrupted") try: image = Image.open(path).convert('RGB') - if width < 2000: + if not varsize: image = image.resize((width, height), PIL.Image.BICUBIC) else: assert batch_size == 1, 'variable img size must have batch size 1' diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 9f96d0fd..110efd19 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -255,7 +255,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat if save_model_every or create_image_every: assert log_directory, "Log directory is empty" -def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding") @@ -309,7 +309,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ pin_memory = shared.opts.pin_memory - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize) if shared.opts.save_training_settings_to_txt: save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()}) diff --git a/modules/ui.py b/modules/ui.py index 99483130..4e709a71 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1343,6 +1343,7 @@ def create_ui(): template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file") training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") + varsize = gr.Checkbox(label="Ignore dimension settings and do not resize images", value=False, elem_id="train_varsize") steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") with FormRow(): @@ -1449,6 +1450,7 @@ def create_ui(): log_directory, training_width, training_height, + varsize, steps, clip_grad_mode, clip_grad_value, @@ -1480,6 +1482,7 @@ def create_ui(): log_directory, training_width, training_height, + varsize, steps, clip_grad_mode, clip_grad_value, -- cgit v1.2.3 From a0c87f1fdf2b76b2ae4ef6c4b01ddaede3afab06 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 8 Jan 2023 08:52:26 +0300 Subject: skip images in embeddings dir if they have a second .preview extension --- modules/textual_inversion/textual_inversion.py | 4 ++++ 1 file changed, 4 insertions(+) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 45882ed6..e85dd549 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -109,6 +109,10 @@ class EmbeddingDatabase: ext = ext.upper() if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']: + _, second_ext = os.path.splitext(name) + if second_ext.upper() == '.PREVIEW': + return + embed_image = Image.open(path) if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: data = embedding_from_b64(embed_image.text['sd-ti-embedding']) -- cgit v1.2.3 From 085427de0efc9e9e7a6e9a5aebc6b5a69f0365e7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 8 Jan 2023 09:37:33 +0300 Subject: make it possible for extensions/scripts to add their own embedding directories --- modules/sd_hijack.py | 7 +- modules/textual_inversion/textual_inversion.py | 170 +++++++++++++++---------- 2 files changed, 108 insertions(+), 69 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index cfdb09d6..6b0d95af 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -83,10 +83,12 @@ class StableDiffusionModelHijack: clip = None optimization_method = None - embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) + embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase() - def hijack(self, m): + def __init__(self): + self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir) + def hijack(self, m): if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: model_embeddings = m.cond_stage_model.roberta.embeddings model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self) @@ -117,7 +119,6 @@ class StableDiffusionModelHijack: self.layers = flatten(m) def undo_hijack(self, m): - if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: m.cond_stage_model = m.cond_stage_model.wrapped diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e85dd549..217fe9eb 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -66,17 +66,41 @@ class Embedding: return self.cached_checksum +class DirWithTextualInversionEmbeddings: + def __init__(self, path): + self.path = path + self.mtime = None + + def has_changed(self): + if not os.path.isdir(self.path): + return False + + mt = os.path.getmtime(self.path) + if self.mtime is None or mt > self.mtime: + return True + + def update(self): + if not os.path.isdir(self.path): + return + + self.mtime = os.path.getmtime(self.path) + + class EmbeddingDatabase: - def __init__(self, embeddings_dir): + def __init__(self): self.ids_lookup = {} self.word_embeddings = {} self.skipped_embeddings = {} - self.dir_mtime = None - self.embeddings_dir = embeddings_dir self.expected_shape = -1 + self.embedding_dirs = {} - def register_embedding(self, embedding, model): + def add_embedding_dir(self, path): + self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path) + + def clear_embedding_dirs(self): + self.embedding_dirs.clear() + def register_embedding(self, embedding, model): self.word_embeddings[embedding.name] = embedding ids = model.cond_stage_model.tokenize([embedding.name])[0] @@ -93,69 +117,62 @@ class EmbeddingDatabase: vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1) return vec.shape[1] - def load_textual_inversion_embeddings(self, force_reload = False): - mt = os.path.getmtime(self.embeddings_dir) - if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime: - return + def load_from_file(self, path, filename): + name, ext = os.path.splitext(filename) + ext = ext.upper() - self.dir_mtime = mt - self.ids_lookup.clear() - self.word_embeddings.clear() - self.skipped_embeddings.clear() - self.expected_shape = self.get_expected_shape() - - def process_file(path, filename): - name, ext = os.path.splitext(filename) - ext = ext.upper() - - if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']: - _, second_ext = os.path.splitext(name) - if second_ext.upper() == '.PREVIEW': - return - - embed_image = Image.open(path) - if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: - data = embedding_from_b64(embed_image.text['sd-ti-embedding']) - name = data.get('name', name) - else: - data = extract_image_data_embed(embed_image) - name = data.get('name', name) - elif ext in ['.BIN', '.PT']: - data = torch.load(path, map_location="cpu") - else: + if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']: + _, second_ext = os.path.splitext(name) + if second_ext.upper() == '.PREVIEW': return - # textual inversion embeddings - if 'string_to_param' in data: - param_dict = data['string_to_param'] - if hasattr(param_dict, '_parameters'): - param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 - assert len(param_dict) == 1, 'embedding file has multiple terms in it' - emb = next(iter(param_dict.items()))[1] - # diffuser concepts - elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: - assert len(data.keys()) == 1, 'embedding file has multiple terms in it' - - emb = next(iter(data.values())) - if len(emb.shape) == 1: - emb = emb.unsqueeze(0) - else: - raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") - - vec = emb.detach().to(devices.device, dtype=torch.float32) - embedding = Embedding(vec, name) - embedding.step = data.get('step', None) - embedding.sd_checkpoint = data.get('sd_checkpoint', None) - embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) - embedding.vectors = vec.shape[0] - embedding.shape = vec.shape[-1] - - if self.expected_shape == -1 or self.expected_shape == embedding.shape: - self.register_embedding(embedding, shared.sd_model) + embed_image = Image.open(path) + if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: + data = embedding_from_b64(embed_image.text['sd-ti-embedding']) + name = data.get('name', name) else: - self.skipped_embeddings[name] = embedding + data = extract_image_data_embed(embed_image) + name = data.get('name', name) + elif ext in ['.BIN', '.PT']: + data = torch.load(path, map_location="cpu") + else: + return + + # textual inversion embeddings + if 'string_to_param' in data: + param_dict = data['string_to_param'] + if hasattr(param_dict, '_parameters'): + param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11 + assert len(param_dict) == 1, 'embedding file has multiple terms in it' + emb = next(iter(param_dict.items()))[1] + # diffuser concepts + elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: + assert len(data.keys()) == 1, 'embedding file has multiple terms in it' + + emb = next(iter(data.values())) + if len(emb.shape) == 1: + emb = emb.unsqueeze(0) + else: + raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") + + vec = emb.detach().to(devices.device, dtype=torch.float32) + embedding = Embedding(vec, name) + embedding.step = data.get('step', None) + embedding.sd_checkpoint = data.get('sd_checkpoint', None) + embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) + embedding.vectors = vec.shape[0] + embedding.shape = vec.shape[-1] + + if self.expected_shape == -1 or self.expected_shape == embedding.shape: + self.register_embedding(embedding, shared.sd_model) + else: + self.skipped_embeddings[name] = embedding - for root, dirs, fns in os.walk(self.embeddings_dir): + def load_from_dir(self, embdir): + if not os.path.isdir(embdir.path): + return + + for root, dirs, fns in os.walk(embdir.path): for fn in fns: try: fullfn = os.path.join(root, fn) @@ -163,12 +180,32 @@ class EmbeddingDatabase: if os.stat(fullfn).st_size == 0: continue - process_file(fullfn, fn) + self.load_from_file(fullfn, fn) except Exception: print(f"Error loading embedding {fn}:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) continue + def load_textual_inversion_embeddings(self, force_reload=False): + if not force_reload: + need_reload = False + for path, embdir in self.embedding_dirs.items(): + if embdir.has_changed(): + need_reload = True + break + + if not need_reload: + return + + self.ids_lookup.clear() + self.word_embeddings.clear() + self.skipped_embeddings.clear() + self.expected_shape = self.get_expected_shape() + + for path, embdir in self.embedding_dirs.items(): + self.load_from_dir(embdir) + embdir.update() + print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}") if len(self.skipped_embeddings) > 0: print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}") @@ -251,14 +288,15 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat assert os.path.isfile(template_file), "Prompt template file doesn't exist" assert steps, "Max steps is empty or 0" assert isinstance(steps, int), "Max steps must be integer" - assert steps > 0 , "Max steps must be positive" + assert steps > 0, "Max steps must be positive" assert isinstance(save_model_every, int), "Save {name} must be integer" - assert save_model_every >= 0 , "Save {name} must be positive or 0" + assert save_model_every >= 0, "Save {name} must be positive or 0" assert isinstance(create_image_every, int), "Create image must be integer" - assert create_image_every >= 0 , "Create image must be positive or 0" + assert create_image_every >= 0, "Create image must be positive or 0" if save_model_every or create_image_every: assert log_directory, "Log directory is empty" + def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 -- cgit v1.2.3 From 43bb5190fc9e7ae479a5dc6640be202c9a71e464 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 9 Jan 2023 22:52:23 +0300 Subject: remove/simplify some changes from #6481 --- modules/textual_inversion/dataset.py | 14 +++++--------- modules/textual_inversion/textual_inversion.py | 4 ++-- modules/ui.py | 2 +- 3 files changed, 8 insertions(+), 12 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index bcad6848..fa48708e 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*") class DatasetEntry: - def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, img_shape=None): + def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None): self.filename = filename self.filename_text = filename_text self.latent_dist = latent_dist @@ -25,7 +25,6 @@ class DatasetEntry: self.cond = cond self.cond_text = cond_text self.pixel_values = pixel_values - self.img_shape = img_shape class PersonalizedBase(Dataset): @@ -46,12 +45,10 @@ class PersonalizedBase(Dataset): assert data_root, 'dataset directory not specified' assert os.path.isdir(data_root), "Dataset directory doesn't exist" assert os.listdir(data_root), "Dataset directory is empty" - if varsize: - assert batch_size == 1, 'variable img size must have batch size 1' + assert batch_size == 1 or not varsize, 'variable img size must have batch size 1' self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] - self.shuffle_tags = shuffle_tags self.tag_drop_out = tag_drop_out @@ -91,14 +88,14 @@ class PersonalizedBase(Dataset): if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)): latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) latent_sampling_method = "once" - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample) elif latent_sampling_method == "deterministic": # Works only for DiagonalGaussianDistribution latent_dist.std = 0 latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample) elif latent_sampling_method == "random": - entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size) + entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist) if not (self.tag_drop_out != 0 or self.shuffle_tags): entry.cond_text = self.create_text(filename_text) @@ -154,7 +151,6 @@ class BatchLoader: self.cond_text = [entry.cond_text for entry in data] self.cond = [entry.cond for entry in data] self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1) - self.img_shape = [entry.img_shape for entry in data] #self.emb_index = [entry.emb_index for entry in data] #print(self.latent_sample.device) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index ad76297e..14be2c96 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -492,8 +492,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ else: p.prompt = batch.cond_text[0] p.steps = 20 - p.width = batch.img_shape[0][0] - p.height = batch.img_shape[0][1] + p.width = training_width + p.height = training_height preview_text = p.prompt diff --git a/modules/ui.py b/modules/ui.py index 9d6b141e..ddfe1b1a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1348,7 +1348,7 @@ def create_ui(): template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file") training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") - varsize = gr.Checkbox(label="Ignore dimension settings and do not resize images", value=False, elem_id="train_varsize") + varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize") steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") with FormRow(): -- cgit v1.2.3 From 1fbb6f9ebe48326a3b12ecf611105dbc4a46891e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 9 Jan 2023 23:35:40 +0300 Subject: make a dropdown for prompt template selection --- modules/hypernetworks/hypernetwork.py | 7 ++++-- modules/shared.py | 1 + modules/textual_inversion/textual_inversion.py | 35 ++++++++++++++++++++------ modules/ui.py | 11 ++++++-- webui.py | 3 +++ 5 files changed, 45 insertions(+), 12 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 32c67ccc..ea3f1db9 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -24,6 +24,7 @@ from statistics import stdev, mean optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} + class HypernetworkModule(torch.nn.Module): multiplier = 1.0 activation_dict = { @@ -403,13 +404,15 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images save_hypernetwork_every = save_hypernetwork_every or 0 create_image_every = create_image_every or 0 - textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + template_file = textual_inversion.textual_inversion_templates.get(template_filename, None) + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + template_file = template_file.path path = shared.hypernetworks.get(hypernetwork_name, None) shared.loaded_hypernetwork = Hypernetwork() diff --git a/modules/shared.py b/modules/shared.py index a1e10201..aa37c8ce 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -33,6 +33,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates") parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 14be2c96..5420903f 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -2,6 +2,7 @@ import os import sys import traceback import inspect +from collections import namedtuple import torch import tqdm @@ -15,12 +16,26 @@ from modules import shared, devices, sd_hijack, processing, sd_models, images, s import modules.textual_inversion.dataset from modules.textual_inversion.learn_schedule import LearnRateScheduler -from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64, - insert_image_data_embed, extract_image_data_embed, - caption_image_overlay) +from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay from modules.textual_inversion.logging import save_settings_to_file +TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"]) +textual_inversion_templates = {} + + +def list_textual_inversion_templates(): + textual_inversion_templates.clear() + + for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir): + for fn in fns: + path = os.path.join(root, fn) + + textual_inversion_templates[fn] = TextualInversionTemplate(fn, path) + + return textual_inversion_templates + + class Embedding: def __init__(self, vec, name, step=None): self.vec = vec @@ -274,7 +289,7 @@ def write_loss(log_directory, filename, step, epoch_len, values): }) -def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"): +def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"): assert model_name, f"{name} not selected" assert learn_rate, "Learning rate is empty or 0" assert isinstance(batch_size, int), "Batch size must be integer" @@ -284,8 +299,9 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat assert data_root, "Dataset directory is empty" assert os.path.isdir(data_root), "Dataset directory doesn't exist" assert os.listdir(data_root), "Dataset directory is empty" - assert template_file, "Prompt template file is empty" - assert os.path.isfile(template_file), "Prompt template file doesn't exist" + assert template_filename, "Prompt template file not selected" + assert template_file, f"Prompt template file {template_filename} not found" + assert os.path.isfile(template_file.path), f"Prompt template file {template_filename} doesn't exist" assert steps, "Max steps is empty or 0" assert isinstance(steps, int), "Max steps must be integer" assert steps > 0, "Max steps must be positive" @@ -296,10 +312,13 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat if save_model_every or create_image_every: assert log_directory, "Log directory is empty" -def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + +def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 - validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding") + template_file = textual_inversion_templates.get(template_filename, None) + validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_embedding_every, create_image_every, log_directory, name="embedding") + template_file = template_file.path shared.state.job = "train-embedding" shared.state.textinfo = "Initializing textual inversion training..." diff --git a/modules/ui.py b/modules/ui.py index ddfe1b1a..b6079aec 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -37,7 +37,7 @@ from modules import prompt_parser from modules.images import save_image from modules.sd_hijack import model_hijack from modules.sd_samplers import samplers, samplers_for_img2img -import modules.textual_inversion.ui +from modules.textual_inversion import textual_inversion import modules.hypernetworks.ui from modules.generation_parameters_copypaste import image_from_url_text @@ -1322,6 +1322,9 @@ def create_ui(): outputs=[process_focal_crop_row], ) + def get_textual_inversion_template_names(): + return sorted([x for x in textual_inversion.textual_inversion_templates]) + with gr.Tab(label="Train"): gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") with FormRow(): @@ -1345,7 +1348,11 @@ def create_ui(): dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory") - template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file") + + with FormRow(): + template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names()) + create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file") + training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize") diff --git a/webui.py b/webui.py index 8737e593..47d372c7 100644 --- a/webui.py +++ b/webui.py @@ -33,6 +33,7 @@ import modules.sd_models import modules.sd_vae import modules.txt2img import modules.script_callbacks +import modules.textual_inversion.textual_inversion import modules.ui from modules import modelloader @@ -67,6 +68,8 @@ def initialize(): modules.sd_vae.refresh_vae_list() + modules.textual_inversion.textual_inversion.list_textual_inversion_templates() + try: modules.sd_models.load_model() except Exception as e: -- cgit v1.2.3 From f9706acf431f77e0ce9e4270e5be7299922ee963 Mon Sep 17 00:00:00 2001 From: Lee Bousfield Date: Tue, 10 Jan 2023 18:40:34 -0700 Subject: Support loading textual inversion embeddings from safetensors files --- modules/textual_inversion/textual_inversion.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5420903f..3866c154 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -9,6 +9,7 @@ import tqdm import html import datetime import csv +import safetensors.torch from PIL import Image, PngImagePlugin @@ -150,6 +151,8 @@ class EmbeddingDatabase: name = data.get('name', name) elif ext in ['.BIN', '.PT']: data = torch.load(path, map_location="cpu") + elif ext in ['.SAFETENSORS']: + data = safetensors.torch.load_file(path, device="cpu") else: return -- cgit v1.2.3 From 3f43d8a966ba8462ba019a5ad573f94508cd45f8 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 11 Jan 2023 10:28:55 -0500 Subject: set descriptions --- modules/hypernetworks/hypernetwork.py | 4 +++- modules/textual_inversion/preprocess.py | 7 ++++++- modules/textual_inversion/textual_inversion.py | 4 +++- 3 files changed, 12 insertions(+), 3 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 300d3975..194679e8 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -619,7 +619,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, epoch_num = hypernetwork.step // steps_per_epoch epoch_step = hypernetwork.step % steps_per_epoch - pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}") + description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" + pbar.set_description(description) + shared.state.textinfo = description if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index feb876c6..3c1042ad 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre params.process_caption_deepbooru = process_caption_deepbooru params.preprocess_txt_action = preprocess_txt_action - for index, imagefile in enumerate(tqdm.tqdm(files)): + pbar = tqdm.tqdm(files) + for index, imagefile in enumerate(pbar): params.subindex = 0 filename = os.path.join(src, imagefile) try: @@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre except Exception: continue + description = f"Preprocessing [Image {index}/{len(files)}]" + pbar.set_description(description) + shared.state.textinfo = description + params.src = filename existing_caption = None diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3866c154..b915b091 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -476,7 +476,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ epoch_num = embedding.step // steps_per_epoch epoch_step = embedding.step % steps_per_epoch - pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}") + description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" + pbar.set_description(description) + shared.state.textinfo = description if embedding_dir is not None and steps_done % save_embedding_every == 0: # Before saving, change name to match current checkpoint. embedding_name_every = f'{embedding_name}-{steps_done}' -- cgit v1.2.3 From d52a80f7f7da160c73afd067c8f1bf491391f994 Mon Sep 17 00:00:00 2001 From: Shondoit Date: Thu, 12 Jan 2023 09:22:29 +0100 Subject: Allow creation of zero vectors for TI --- modules/textual_inversion/textual_inversion.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index b915b091..853246a6 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -248,11 +248,14 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): with devices.autocast(): cond_model([""]) # will send cond model to GPU if lowvram/medvram is active - embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token) + #cond_model expects at least some text, so we provide '*' as backup. + embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token) vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device) - for i in range(num_vectors_per_token): - vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] + #Only copy if we provided an init_text, otherwise keep vectors as zeros + if init_text: + for i in range(num_vectors_per_token): + vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] # Remove illegal characters from name. name = "".join( x for x in name if (x.isalnum() or x in "._- ")) -- cgit v1.2.3 From a176d89487d92f5a5b152401e5c424b34ff43b96 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 13 Jan 2023 14:32:15 +0300 Subject: print bucket sizes for training without resizing images #6620 fix an error when generating a picture with embedding in it --- modules/textual_inversion/dataset.py | 16 ++++++++++++++++ modules/textual_inversion/image_embedding.py | 4 ++-- modules/textual_inversion/textual_inversion.py | 2 +- 3 files changed, 19 insertions(+), 3 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index b47414f3..d31963d4 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -118,6 +118,12 @@ class PersonalizedBase(Dataset): self.gradient_step = min(gradient_step, self.length // self.batch_size) self.latent_sampling_method = latent_sampling_method + if len(groups) > 1: + print("Buckets:") + for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]): + print(f" {w}x{h}: {len(ids)}") + print() + def create_text(self, filename_text): text = random.choice(self.lines) tags = filename_text.split(',') @@ -140,8 +146,11 @@ class PersonalizedBase(Dataset): entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu) return entry + class GroupedBatchSampler(Sampler): def __init__(self, data_source: PersonalizedBase, batch_size: int): + super().__init__(data_source) + n = len(data_source) self.groups = data_source.groups self.len = n_batch = n // batch_size @@ -150,21 +159,28 @@ class GroupedBatchSampler(Sampler): self.n_rand_batches = nrb = n_batch - sum(self.base) self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected] self.batch_size = batch_size + def __len__(self): return self.len + def __iter__(self): b = self.batch_size + for g in self.groups: shuffle(g) + batches = [] for g in self.groups: batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b)) for _ in range(self.n_rand_batches): rand_group = choices(self.groups, self.probs)[0] batches.append(choices(rand_group, k=b)) + shuffle(batches) + yield from batches + class PersonalizedDataLoader(DataLoader): def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False): super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index ea653806..5593f88c 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -76,10 +76,10 @@ def insert_image_data_embed(image, data): next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h)) next_size = next_size + ((h*d)-(next_size % (h*d))) - data_np_low.resize(next_size) + data_np_low = np.resize(data_np_low, next_size) data_np_low = data_np_low.reshape((h, -1, d)) - data_np_high.resize(next_size) + data_np_high = np.resize(data_np_high, next_size) data_np_high = data_np_high.reshape((h, -1, d)) edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024] diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 853246a6..e23906ca 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -479,7 +479,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ epoch_num = embedding.step // steps_per_epoch epoch_step = embedding.step % steps_per_epoch - description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" + description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}" pbar.set_description(description) shared.state.textinfo = description if embedding_dir is not None and steps_done % save_embedding_every == 0: -- cgit v1.2.3 From 82725f0ac439f7e3b67858d55900e95330bbd326 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 13 Jan 2023 15:04:37 +0300 Subject: fix a bug caused by merge --- modules/textual_inversion/textual_inversion.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 85210b0e..6939efcc 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -11,6 +11,7 @@ import datetime import csv import safetensors.torch +import numpy as np from PIL import Image, PngImagePlugin from torch.utils.tensorboard import SummaryWriter -- cgit v1.2.3 From a95f1353089bdeaccd7c266b40cdd79efedfe632 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 14 Jan 2023 09:56:59 +0300 Subject: change hash to sha256 --- .gitignore | 1 + modules/api/api.py | 2 +- modules/api/models.py | 3 +- modules/hashes.py | 72 +++++++++++++++ modules/hypernetworks/hypernetwork.py | 4 +- modules/sd_models.py | 116 ++++++++++++++++--------- modules/shared.py | 2 +- modules/textual_inversion/textual_inversion.py | 6 +- webui.py | 2 + 9 files changed, 158 insertions(+), 50 deletions(-) create mode 100644 modules/hashes.py (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/.gitignore b/.gitignore index 21fa26a7..0b1d17ca 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,4 @@ notification.mp3 /extensions /test/stdout.txt /test/stderr.txt +/cache.json diff --git a/modules/api/api.py b/modules/api/api.py index 5767ba90..9814bbc2 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -371,7 +371,7 @@ class Api: return upscalers def get_sd_models(self): - return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()] + return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()] def get_hypernetworks(self): return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] diff --git a/modules/api/models.py b/modules/api/models.py index c78095ca..1eb1fcf1 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -224,7 +224,8 @@ class UpscalerItem(BaseModel): class SDModelItem(BaseModel): title: str = Field(title="Title") model_name: str = Field(title="Model Name") - hash: str = Field(title="Hash") + hash: Optional[str] = Field(title="Short hash") + sha256: Optional[str] = Field(title="sha256 hash") filename: str = Field(title="Filename") config: str = Field(title="Config file") diff --git a/modules/hashes.py b/modules/hashes.py new file mode 100644 index 00000000..ebfbd90c --- /dev/null +++ b/modules/hashes.py @@ -0,0 +1,72 @@ +import hashlib +import json +import os.path + +import filelock + + +cache_filename = "cache.json" +cache_data = None + + +def dump_cache(): + with filelock.FileLock(cache_filename+".lock"): + with open(cache_filename, "w", encoding="utf8") as file: + json.dump(cache_data, file, indent=4) + + +def cache(subsection): + global cache_data + + if cache_data is None: + with filelock.FileLock(cache_filename+".lock"): + if not os.path.isfile(cache_filename): + cache_data = {} + else: + with open(cache_filename, "r", encoding="utf8") as file: + cache_data = json.load(file) + + s = cache_data.get(subsection, {}) + cache_data[subsection] = s + + return s + + +def calculate_sha256(filename): + hash_sha256 = hashlib.sha256() + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + + +def sha256(filename, title): + hashes = cache("hashes") + ondisk_mtime = os.path.getmtime(filename) + + if title in hashes: + cached_sha256 = hashes[title].get("sha256", None) + cached_mtime = hashes[title].get("mtime", 0) + + if ondisk_mtime <= cached_mtime and cached_sha256 is not None: + return cached_sha256 + + print(f"Calculating sha256 for {filename}: ", end='') + sha256_value = calculate_sha256(filename) + print(f"{sha256_value}") + + hashes[title] = { + "mtime": ondisk_mtime, + "sha256": sha256_value, + } + + dump_cache() + + return sha256_value + + + + + diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 83cbb4f0..9b5f2e79 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -509,7 +509,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, if shared.opts.save_training_settings_to_txt: saved_params = dict( - model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), + model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]} ) logging.save_settings_to_file(log_directory, {**saved_params, **locals()}) @@ -737,7 +737,7 @@ def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None try: - hypernetwork.sd_checkpoint = checkpoint.hash + hypernetwork.sd_checkpoint = checkpoint.shorthash hypernetwork.sd_checkpoint_name = checkpoint.model_name hypernetwork.name = hypernetwork_name hypernetwork.save(filename) diff --git a/modules/sd_models.py b/modules/sd_models.py index c466f273..7babb9ae 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -14,17 +14,56 @@ import ldm.modules.midas as midas from ldm.util import instantiate_from_config -from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors +from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes from modules.paths import models_path from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) -CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name']) checkpoints_list = {} +checkpoint_alisases = {} checkpoints_loaded = collections.OrderedDict() + +class CheckpointInfo: + def __init__(self, filename): + self.filename = filename + abspath = os.path.abspath(filename) + + if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir): + name = abspath.replace(shared.cmd_opts.ckpt_dir, '') + elif abspath.startswith(model_path): + name = abspath.replace(model_path, '') + else: + name = os.path.basename(filename) + + if name.startswith("\\") or name.startswith("/"): + name = name[1:] + + self.title = name + self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] + self.hash = model_hash(filename) + self.ids = [self.hash, self.model_name, self.title, f'{name} [{self.hash}]'] + self.shorthash = None + self.sha256 = None + + def register(self): + checkpoints_list[self.title] = self + for id in self.ids: + checkpoint_alisases[id] = self + + def calculate_shorthash(self): + self.sha256 = hashes.sha256(self.filename, self.title) + self.shorthash = self.sha256[0:10] + + if self.shorthash not in self.ids: + self.ids += [self.shorthash, self.sha256] + self.register() + + return self.shorthash + + try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. @@ -43,10 +82,14 @@ def setup_model(): enable_midas_autodownload() -def checkpoint_tiles(): - convert = lambda name: int(name) if name.isdigit() else name.lower() - alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] - return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key) +def checkpoint_tiles(): + def convert(name): + return int(name) if name.isdigit() else name.lower() + + def alphanumeric_key(key): + return [convert(c) for c in re.split('([0-9]+)', key)] + + return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key) def find_checkpoint_config(info): @@ -62,48 +105,38 @@ def find_checkpoint_config(info): def list_models(): checkpoints_list.clear() + checkpoint_alisases.clear() model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"]) - def modeltitle(path, shorthash): - abspath = os.path.abspath(path) - - if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir): - name = abspath.replace(shared.cmd_opts.ckpt_dir, '') - elif abspath.startswith(model_path): - name = abspath.replace(model_path, '') - else: - name = os.path.basename(path) - - if name.startswith("\\") or name.startswith("/"): - name = name[1:] - - shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0] - - return f'{name} [{shorthash}]', shortname - cmd_ckpt = shared.cmd_opts.ckpt if os.path.exists(cmd_ckpt): - h = model_hash(cmd_ckpt) - title, short_model_name = modeltitle(cmd_ckpt, h) - checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name) - shared.opts.data['sd_model_checkpoint'] = title + checkpoint_info = CheckpointInfo(cmd_ckpt) + checkpoint_info.register() + + shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file: print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr) + for filename in model_list: - h = model_hash(filename) - title, short_model_name = modeltitle(filename, h) + checkpoint_info = CheckpointInfo(filename) + checkpoint_info.register() + - checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name) +def get_closet_checkpoint_match(search_string): + checkpoint_info = checkpoint_alisases.get(search_string, None) + if checkpoint_info is not None: + return + found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title)) + if found: + return found[0] -def get_closet_checkpoint_match(searchString): - applicable = sorted([info for info in checkpoints_list.values() if searchString in info.title], key = lambda x:len(x.title)) - if len(applicable) > 0: - return applicable[0] return None def model_hash(filename): + """old hash that only looks at a small part of the file and is prone to collisions""" + try: with open(filename, "rb") as file: import hashlib @@ -119,7 +152,7 @@ def model_hash(filename): def select_checkpoint(): model_checkpoint = shared.opts.sd_model_checkpoint - checkpoint_info = checkpoints_list.get(model_checkpoint, None) + checkpoint_info = checkpoint_alisases.get(model_checkpoint, None) if checkpoint_info is not None: return checkpoint_info @@ -189,9 +222,8 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None return sd -def load_model_weights(model, checkpoint_info, vae_file="auto"): - checkpoint_file = checkpoint_info.filename - sd_model_hash = checkpoint_info.hash +def load_model_weights(model, checkpoint_info: CheckpointInfo, vae_file="auto"): + sd_model_hash = checkpoint_info.calculate_shorthash() cache_enabled = shared.opts.sd_checkpoint_cache > 0 @@ -201,9 +233,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.load_state_dict(checkpoints_loaded[checkpoint_info]) else: # load from file - print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}") + print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}") - sd = read_state_dict(checkpoint_file) + sd = read_state_dict(checkpoint_info.filename) model.load_state_dict(sd, strict=False) del sd @@ -235,14 +267,14 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): checkpoints_loaded.popitem(last=False) # LRU model.sd_model_hash = sd_model_hash - model.sd_model_checkpoint = checkpoint_file + model.sd_model_checkpoint = checkpoint_info.filename model.sd_checkpoint_info = checkpoint_info model.logvar = model.logvar.to(devices.device) # fix for training sd_vae.delete_base_vae() sd_vae.clear_loaded_vae() - vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) + vae_file = sd_vae.resolve_vae(checkpoint_info.filename, vae_file=vae_file) sd_vae.load_vae(model, vae_file) diff --git a/modules/shared.py b/modules/shared.py index b90ded52..d74c069d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -428,7 +428,7 @@ options_templates.update(options_section(('ui', "User interface"), { "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), - "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"), + "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"), "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."), "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"), "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"), diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 6939efcc..63935878 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -407,7 +407,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize) if shared.opts.save_training_settings_to_txt: - save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()}) + save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()}) latent_sampling_method = ds.latent_sampling_method @@ -584,7 +584,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ checkpoint = sd_models.select_checkpoint() footer_left = checkpoint.model_name - footer_mid = '[{}]'.format(checkpoint.hash) + footer_mid = '[{}]'.format(checkpoint.shorthash) footer_right = '{}v {}s'.format(vectorSize, steps_done) captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right) @@ -626,7 +626,7 @@ def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, r old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None try: - embedding.sd_checkpoint = checkpoint.hash + embedding.sd_checkpoint = checkpoint.shorthash embedding.sd_checkpoint_name = checkpoint.model_name if remove_cached_checksum: embedding.cached_checksum = None diff --git a/webui.py b/webui.py index 47d372c7..1fff80da 100644 --- a/webui.py +++ b/webui.py @@ -78,6 +78,8 @@ def initialize(): print("Stable diffusion model failed to load, exiting", file=sys.stderr) exit(1) + 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())) shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) -- cgit v1.2.3 From d8b90ac121cbf0c18b1dc9d56a5e1d14ca51e74e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sun, 15 Jan 2023 18:50:56 +0300 Subject: big rework of progressbar/preview system to allow multiple users to prompts at the same time and do not get previews of each other --- javascript/progressbar.js | 249 ++++++++++++++++--------- javascript/textualInversion.js | 13 +- javascript/ui.js | 33 +++- modules/call_queue.py | 19 +- modules/hypernetworks/hypernetwork.py | 6 +- modules/img2img.py | 2 +- modules/progress.py | 96 ++++++++++ modules/sd_samplers.py | 2 +- modules/shared.py | 16 +- modules/textual_inversion/preprocess.py | 2 +- modules/textual_inversion/textual_inversion.py | 6 +- modules/txt2img.py | 2 +- modules/ui.py | 41 ++-- modules/ui_progress.py | 101 ---------- style.css | 74 +++++--- webui.py | 3 + 16 files changed, 390 insertions(+), 275 deletions(-) create mode 100644 modules/progress.py delete mode 100644 modules/ui_progress.py (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/javascript/progressbar.js b/javascript/progressbar.js index d6323ed9..b7524ef7 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,82 +1,25 @@ // code related to showing and updating progressbar shown as the image is being made -global_progressbars = {} -galleries = {} -galleryObservers = {} - -// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running -timeoutIds = {} -function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ - // gradio 3.8's enlightened approach allows them to create two nested div elements inside each other with same id - // every time you use gr.HTML(elem_id='xxx'), so we handle this here - var progressbar = gradioApp().querySelector("#"+id_progressbar+" #"+id_progressbar) - var progressbarParent - if(progressbar){ - progressbarParent = gradioApp().querySelector("#"+id_progressbar) - } else{ - progressbar = gradioApp().getElementById(id_progressbar) - progressbarParent = null - } - var skip = id_skip ? gradioApp().getElementById(id_skip) : null - var interrupt = gradioApp().getElementById(id_interrupt) - - if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ - if(progressbar.innerText){ - let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion'; - if(document.title != newtitle){ - document.title = newtitle; - } - }else{ - let newtitle = 'Stable Diffusion' - if(document.title != newtitle){ - document.title = newtitle; - } - } - } - - if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){ - global_progressbars[id_progressbar] = progressbar - - var mutationObserver = new MutationObserver(function(m){ - if(timeoutIds[id_part]) return; - - preview = gradioApp().getElementById(id_preview) - gallery = gradioApp().getElementById(id_gallery) +galleries = {} +storedGallerySelections = {} +galleryObservers = {} - if(preview != null && gallery != null){ - preview.style.width = gallery.clientWidth + "px" - preview.style.height = gallery.clientHeight + "px" - if(progressbarParent) progressbar.style.width = progressbarParent.clientWidth + "px" +function rememberGallerySelection(id_gallery){ + storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery) +} - //only watch gallery if there is a generation process going on - check_gallery(id_gallery); +function getGallerySelectedIndex(id_gallery){ + let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') + let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; - if(progressDiv){ - timeoutIds[id_part] = window.setTimeout(function() { - timeoutIds[id_part] = null - requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) - }, 500) - } else{ - if (skip) { - skip.style.display = "none" - } - interrupt.style.display = "none" + let currentlySelectedIndex = -1 + galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } }) - //disconnect observer once generation finished, so user can close selected image if they want - if (galleryObservers[id_gallery]) { - galleryObservers[id_gallery].disconnect(); - galleries[id_gallery] = null; - } - } - } - - }); - mutationObserver.observe( progressbar, { childList:true, subtree:true }) - } + return currentlySelectedIndex } +// this is a workaround for https://github.com/gradio-app/gradio/issues/2984 function check_gallery(id_gallery){ let gallery = gradioApp().getElementById(id_gallery) // if gallery has no change, no need to setting up observer again. @@ -85,10 +28,16 @@ function check_gallery(id_gallery){ if(galleryObservers[id_gallery]){ galleryObservers[id_gallery].disconnect(); } - let prevSelectedIndex = selected_gallery_index(); + + storedGallerySelections[id_gallery] = -1 + galleryObservers[id_gallery] = new MutationObserver(function (){ let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') + let currentlySelectedIndex = getGallerySelectedIndex(id_gallery) + prevSelectedIndex = storedGallerySelections[id_gallery] + storedGallerySelections[id_gallery] = -1 + if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) { // automatically re-open previously selected index (if exists) activeElement = gradioApp().activeElement; @@ -120,30 +69,150 @@ function check_gallery(id_gallery){ } onUiUpdate(function(){ - check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') - check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') - check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery') + check_gallery('txt2img_gallery') + check_gallery('img2img_gallery') }) -function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){ - btn = gradioApp().getElementById(id_part+"_check_progress"); - if(btn==null) return; - - btn.click(); - var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; - var skip = id_skip ? gradioApp().getElementById(id_skip) : null - var interrupt = gradioApp().getElementById(id_interrupt) - if(progressDiv && interrupt){ - if (skip) { - skip.style.display = "block" +function request(url, data, handler, errorHandler){ + var xhr = new XMLHttpRequest(); + var url = url; + xhr.open("POST", url, true); + xhr.setRequestHeader("Content-Type", "application/json"); + xhr.onreadystatechange = function () { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + var js = JSON.parse(xhr.responseText); + handler(js) + } else{ + errorHandler() + } } - interrupt.style.display = "block" + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function pad2(x){ + return x<10 ? '0'+x : x +} + +function formatTime(secs){ + if(secs > 3600){ + return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60) + } else if(secs > 60){ + return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60) + } else{ + return Math.floor(secs) + "s" } } -function requestProgress(id_part){ - btn = gradioApp().getElementById(id_part+"_check_progress_initial"); - if(btn==null) return; +function randomId(){ + return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")" +} + +// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and +// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd. +// calls onProgress every time there is a progress update +function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){ + var dateStart = new Date() + var wasEverActive = false + var parentProgressbar = progressbarContainer.parentNode + var parentGallery = gallery.parentNode + + var divProgress = document.createElement('div') + divProgress.className='progressDiv' + var divInner = document.createElement('div') + divInner.className='progress' + + divProgress.appendChild(divInner) + parentProgressbar.insertBefore(divProgress, progressbarContainer) + + var livePreview = document.createElement('div') + livePreview.className='livePreview' + parentGallery.insertBefore(livePreview, gallery) + + var removeProgressBar = function(){ + parentProgressbar.removeChild(divProgress) + parentGallery.removeChild(livePreview) + atEnd() + } + + var fun = function(id_task, id_live_preview){ + request("/internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){ + console.log(res) + + if(res.completed){ + removeProgressBar() + return + } + + var rect = progressbarContainer.getBoundingClientRect() + + if(rect.width){ + divProgress.style.width = rect.width + "px"; + } + + progressText = "" + + divInner.style.width = ((res.progress || 0) * 100.0) + '%' + + if(res.progress > 0){ + progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%' + } + + if(res.eta){ + progressText += " ETA: " + formatTime(res.eta) + } else if(res.textinfo){ + progressText += " " + res.textinfo + } + + divInner.textContent = progressText + + var elapsedFromStart = (new Date() - dateStart) / 1000 + + if(res.active) wasEverActive = true; + + if(! res.active && wasEverActive){ + removeProgressBar() + return + } + + if(elapsedFromStart > 5 && !res.queued && !res.active){ + removeProgressBar() + return + } + + + if(res.live_preview){ + var img = new Image(); + img.onload = function() { + var rect = gallery.getBoundingClientRect() + if(rect.width){ + livePreview.style.width = rect.width + "px" + livePreview.style.height = rect.height + "px" + } + + livePreview.innerHTML = '' + livePreview.appendChild(img) + if(livePreview.childElementCount > 2){ + livePreview.removeChild(livePreview.firstElementChild) + } + } + img.src = res.live_preview; + } + + + if(onProgress){ + onProgress(res) + } + + setTimeout(() => { + fun(id_task, res.id_live_preview); + }, 500) + }, function(){ + removeProgressBar() + }) + } - btn.click(); + fun(id_task, 0) } diff --git a/javascript/textualInversion.js b/javascript/textualInversion.js index 8061be08..0354b860 100644 --- a/javascript/textualInversion.js +++ b/javascript/textualInversion.js @@ -1,8 +1,17 @@ + function start_training_textual_inversion(){ - requestProgress('ti') gradioApp().querySelector('#ti_error').innerHTML='' - return args_to_array(arguments) + var id = randomId() + requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function(){}, function(progress){ + gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo + }) + + var res = args_to_array(arguments) + + res[0] = id + + return res } diff --git a/javascript/ui.js b/javascript/ui.js index f8279124..ecf97cb3 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -126,18 +126,41 @@ function create_submit_args(args){ return res } +function showSubmitButtons(tabname, show){ + gradioApp().getElementById(tabname+'_interrupt').style.display = show ? "none" : "block" + gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block" +} + function submit(){ - requestProgress('txt2img') + rememberGallerySelection('txt2img_gallery') + showSubmitButtons('txt2img', false) + + var id = randomId() + requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){ + showSubmitButtons('txt2img', true) + + }) - return create_submit_args(arguments) + var res = create_submit_args(arguments) + + res[0] = id + + return res } function submit_img2img(){ - requestProgress('img2img') + rememberGallerySelection('img2img_gallery') + showSubmitButtons('img2img', false) + + var id = randomId() + requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){ + showSubmitButtons('img2img', true) + }) - res = create_submit_args(arguments) + var res = create_submit_args(arguments) - res[0] = get_tab_index('mode_img2img') + res[0] = id + res[1] = get_tab_index('mode_img2img') return res } diff --git a/modules/call_queue.py b/modules/call_queue.py index 4cd49533..92097c15 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -4,7 +4,7 @@ import threading import traceback import time -from modules import shared +from modules import shared, progress queue_lock = threading.Lock() @@ -22,12 +22,23 @@ def wrap_queued_call(func): def wrap_gradio_gpu_call(func, extra_outputs=None): def f(*args, **kwargs): - shared.state.begin() + # if the first argument is a string that says "task(...)", it is treated as a job id + if len(args) > 0 and type(args[0]) == str and args[0][0:5] == "task(" and args[0][-1] == ")": + id_task = args[0] + progress.add_task_to_queue(id_task) + else: + id_task = None with queue_lock: - res = func(*args, **kwargs) + shared.state.begin() + progress.start_task(id_task) + + try: + res = func(*args, **kwargs) + finally: + progress.finish_task(id_task) - shared.state.end() + shared.state.end() return res diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3aebefa8..ae6af516 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -453,7 +453,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images @@ -629,7 +629,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" pbar.set_description(description) - shared.state.textinfo = description if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: # Before saving, change name to match current checkpoint. hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' @@ -701,7 +700,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, torch.cuda.set_rng_state_all(cuda_rng_state) hypernetwork.train() if image is not None: - shared.state.current_image = image + shared.state.assign_current_image(image) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" diff --git a/modules/img2img.py b/modules/img2img.py index f62783c6..f4a03c57 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -59,7 +59,7 @@ def process_batch(p, input_dir, output_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): +def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): is_batch = mode == 5 if mode == 0: # img2img diff --git a/modules/progress.py b/modules/progress.py new file mode 100644 index 00000000..3327b883 --- /dev/null +++ b/modules/progress.py @@ -0,0 +1,96 @@ +import base64 +import io +import time + +import gradio as gr +from pydantic import BaseModel, Field + +from modules.shared import opts + +import modules.shared as shared + + +current_task = None +pending_tasks = {} +finished_tasks = [] + + +def start_task(id_task): + global current_task + + current_task = id_task + pending_tasks.pop(id_task, None) + + +def finish_task(id_task): + global current_task + + if current_task == id_task: + current_task = None + + finished_tasks.append(id_task) + if len(finished_tasks) > 16: + finished_tasks.pop(0) + + +def add_task_to_queue(id_job): + pending_tasks[id_job] = time.time() + + +class ProgressRequest(BaseModel): + id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for") + id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image") + + +class ProgressResponse(BaseModel): + active: bool = Field(title="Whether the task is being worked on right now") + queued: bool = Field(title="Whether the task is in queue") + completed: bool = Field(title="Whether the task has already finished") + progress: float = Field(default=None, title="Progress", description="The progress with a range of 0 to 1") + eta: float = Field(default=None, title="ETA in secs") + live_preview: str = Field(default=None, title="Live preview image", description="Current live preview; a data: uri") + id_live_preview: int = Field(default=None, title="Live preview image ID", description="Send this together with next request to prevent receiving same image") + textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.") + + +def setup_progress_api(app): + return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=ProgressResponse) + + +def progressapi(req: ProgressRequest): + active = req.id_task == current_task + queued = req.id_task in pending_tasks + completed = req.id_task in finished_tasks + + if not active: + return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo="In queue..." if queued else "Waiting...") + + progress = 0 + + if shared.state.job_count > 0: + progress += shared.state.job_no / shared.state.job_count + if shared.state.sampling_steps > 0: + progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + + progress = min(progress, 1) + + elapsed_since_start = time.time() - shared.state.time_start + predicted_duration = elapsed_since_start / progress if progress > 0 else None + eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None + + id_live_preview = req.id_live_preview + shared.state.set_current_image() + if opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview: + image = shared.state.current_image + if image is not None: + buffered = io.BytesIO() + image.save(buffered, format="png") + live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii") + id_live_preview = shared.state.id_live_preview + else: + live_preview = None + else: + live_preview = None + + return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo) + diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 7616fded..76e0e0d5 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -140,7 +140,7 @@ def store_latent(decoded): if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0: if not shared.parallel_processing_allowed: - shared.state.current_image = sample_to_image(decoded) + shared.state.assign_current_image(sample_to_image(decoded)) class InterruptedException(BaseException): diff --git a/modules/shared.py b/modules/shared.py index 51df056c..de99aca9 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -152,6 +152,7 @@ def reload_hypernetworks(): hypernetwork.load_hypernetwork(opts.sd_hypernetwork) + class State: skipped = False interrupted = False @@ -165,6 +166,7 @@ class State: current_latent = None current_image = None current_image_sampling_step = 0 + id_live_preview = 0 textinfo = None time_start = None need_restart = False @@ -207,6 +209,7 @@ class State: self.current_latent = None self.current_image = None self.current_image_sampling_step = 0 + self.id_live_preview = 0 self.skipped = False self.interrupted = False self.textinfo = None @@ -220,8 +223,8 @@ class State: devices.torch_gc() - """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this""" def set_current_image(self): + """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this""" if not parallel_processing_allowed: return @@ -234,12 +237,16 @@ class State: import modules.sd_samplers if opts.show_progress_grid: - self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent) + self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) else: - self.current_image = modules.sd_samplers.sample_to_image(self.current_latent) + self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent)) self.current_image_sampling_step = self.sampling_step + def assign_current_image(self, image): + self.current_image = image + self.id_live_preview += 1 + state = State() state.server_start = time.time() @@ -424,8 +431,6 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), })) options_templates.update(options_section(('ui', "User interface"), { - "show_progressbar": OptionInfo(True, "Show progressbar"), - "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), @@ -446,6 +451,7 @@ options_templates.update(options_section(('ui', "User interface"), { options_templates.update(options_section(('ui', "Live previews"), { "live_previews_enable": OptionInfo(True, "Show live previews of the created image"), + "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), "show_progress_every_n_steps": OptionInfo(10, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}), "show_progress_type": OptionInfo("Approx NN", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}), "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}), diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 3c1042ad..64abff4d 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -12,7 +12,7 @@ from modules.shared import opts, cmd_opts from modules.textual_inversion import autocrop -def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False): +def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False): try: if process_caption: shared.interrogator.load() diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 63935878..7e4a6d24 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -345,7 +345,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat assert log_directory, "Log directory is empty" -def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 template_file = textual_inversion_templates.get(template_filename, None) @@ -510,7 +510,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}" pbar.set_description(description) - shared.state.textinfo = description if embedding_dir is not None and steps_done % save_embedding_every == 0: # Before saving, change name to match current checkpoint. embedding_name_every = f'{embedding_name}-{steps_done}' @@ -560,7 +559,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ shared.sd_model.first_stage_model.to(devices.cpu) if image is not None: - shared.state.current_image = image + shared.state.assign_current_image(image) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) last_saved_image += f", prompt: {preview_text}" diff --git a/modules/txt2img.py b/modules/txt2img.py index 38b5f591..ca5d4550 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -8,7 +8,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, *args): +def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, diff --git a/modules/ui.py b/modules/ui.py index 2425c66f..ff33236b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -356,7 +356,7 @@ def create_toprow(is_img2img): button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru") with gr.Column(scale=1): - with gr.Row(): + with gr.Row(elem_id=f"{id_part}_generate_box"): skip = gr.Button('Skip', elem_id=f"{id_part}_skip") interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt") submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary') @@ -384,9 +384,7 @@ def create_toprow(is_img2img): def setup_progressbar(*args, **kwargs): - import modules.ui_progress - - modules.ui_progress.setup_progressbar(*args, **kwargs) + pass def apply_setting(key, value): @@ -479,8 +477,8 @@ Requested path was: {f} else: sp.Popen(["xdg-open", path]) - with gr.Column(variant='panel'): - with gr.Group(): + with gr.Column(variant='panel', elem_id=f"{tabname}_results"): + with gr.Group(elem_id=f"{tabname}_gallery_container"): result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4) generation_info = None @@ -595,15 +593,6 @@ def create_ui(): dummy_component = gr.Label(visible=False) txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) - with gr.Row(elem_id='txt2img_progress_row'): - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="txt2img_progressbar") - txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False) - setup_progressbar(progressbar, txt2img_preview, 'txt2img') - with gr.Row().style(equal_height=False): with gr.Column(variant='panel', elem_id="txt2img_settings"): for category in ordered_ui_categories(): @@ -682,6 +671,7 @@ def create_ui(): fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']), _js="submit", inputs=[ + dummy_component, txt2img_prompt, txt2img_negative_prompt, txt2img_prompt_style, @@ -782,16 +772,7 @@ def create_ui(): with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True) - with gr.Row(elem_id='img2img_progress_row'): - img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) - - with gr.Column(scale=1): - pass - - with gr.Column(scale=1): - progressbar = gr.HTML(elem_id="img2img_progressbar") - img2img_preview = gr.Image(elem_id='img2img_preview', visible=False) - setup_progressbar(progressbar, img2img_preview, 'img2img') + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) with FormRow().style(equal_height=False): with gr.Column(variant='panel', elem_id="img2img_settings"): @@ -958,6 +939,7 @@ def create_ui(): fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']), _js="submit_img2img", inputs=[ + dummy_component, dummy_component, img2img_prompt, img2img_negative_prompt, @@ -1335,15 +1317,11 @@ def create_ui(): script_callbacks.ui_train_tabs_callback(params) - with gr.Column(): - progressbar = gr.HTML(elem_id="ti_progressbar") + with gr.Column(elem_id='ti_gallery_container'): ti_output = gr.Text(elem_id="ti_output", value="", show_label=False) - ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4) - ti_preview = gr.Image(elem_id='ti_preview', visible=False) ti_progress = gr.HTML(elem_id="ti_progress", value="") ti_outcome = gr.HTML(elem_id="ti_error", value="") - setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress) create_embedding.click( fn=modules.textual_inversion.ui.create_embedding, @@ -1384,6 +1362,7 @@ def create_ui(): fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ + dummy_component, process_src, process_dst, process_width, @@ -1411,6 +1390,7 @@ def create_ui(): fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ + dummy_component, train_embedding_name, embedding_learn_rate, batch_size, @@ -1443,6 +1423,7 @@ def create_ui(): fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]), _js="start_training_textual_inversion", inputs=[ + dummy_component, train_hypernetwork_name, hypernetwork_learn_rate, batch_size, diff --git a/modules/ui_progress.py b/modules/ui_progress.py deleted file mode 100644 index 7cd312e4..00000000 --- a/modules/ui_progress.py +++ /dev/null @@ -1,101 +0,0 @@ -import time - -import gradio as gr - -from modules.shared import opts - -import modules.shared as shared - - -def calc_time_left(progress, threshold, label, force_display, show_eta): - if progress == 0: - return "" - else: - time_since_start = time.time() - shared.state.time_start - eta = (time_since_start/progress) - eta_relative = eta-time_since_start - if (eta_relative > threshold and show_eta) or force_display: - if eta_relative > 3600: - return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) - elif eta_relative > 60: - return label + time.strftime('%M:%S', time.gmtime(eta_relative)) - else: - return label + time.strftime('%Ss', time.gmtime(eta_relative)) - else: - return "" - - -def check_progress_call(id_part): - if shared.state.job_count == 0: - return "", gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) - - progress = 0 - - if shared.state.job_count > 0: - progress += shared.state.job_no / shared.state.job_count - if shared.state.sampling_steps > 0: - progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps - - # Show progress percentage and time left at the same moment, and base it also on steps done - show_eta = progress >= 0.01 or shared.state.sampling_step >= 10 - - time_left = calc_time_left(progress, 1, " ETA: ", shared.state.time_left_force_display, show_eta) - if time_left != "": - shared.state.time_left_force_display = True - - progress = min(progress, 1) - - progressbar = "" - if opts.show_progressbar: - progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if show_eta else ""}
""" - - image = gr.update(visible=False) - preview_visibility = gr.update(visible=False) - - if opts.live_previews_enable: - shared.state.set_current_image() - image = shared.state.current_image - - if image is None: - image = gr.update(value=None) - else: - preview_visibility = gr.update(visible=True) - - if shared.state.textinfo is not None: - textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True) - else: - textinfo_result = gr.update(visible=False) - - return f"

{progressbar}

", preview_visibility, image, textinfo_result - - -def check_progress_call_initial(id_part): - shared.state.job_count = -1 - shared.state.current_latent = None - shared.state.current_image = None - shared.state.textinfo = None - shared.state.time_start = time.time() - shared.state.time_left_force_display = False - - return check_progress_call(id_part) - - -def setup_progressbar(progressbar, preview, id_part, textinfo=None): - if textinfo is None: - textinfo = gr.HTML(visible=False) - - check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False) - check_progress.click( - fn=lambda: check_progress_call(id_part), - show_progress=False, - inputs=[], - outputs=[progressbar, preview, preview, textinfo], - ) - - check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False) - check_progress_initial.click( - fn=lambda: check_progress_call_initial(id_part), - show_progress=False, - inputs=[], - outputs=[progressbar, preview, preview, textinfo], - ) diff --git a/style.css b/style.css index 2d484e06..786b71d1 100644 --- a/style.css +++ b/style.css @@ -305,26 +305,42 @@ input[type="range"]{ } .progressDiv{ - width: 100%; - height: 20px; - background: #b4c0cc; - border-radius: 8px; + position: absolute; + height: 20px; + top: -20px; + background: #b4c0cc; + border-radius: 8px !important; } .dark .progressDiv{ - background: #424c5b; + background: #424c5b; } .progressDiv .progress{ - width: 0%; - height: 20px; - background: #0060df; - color: white; - font-weight: bold; - line-height: 20px; - padding: 0 8px 0 0; - text-align: right; - border-radius: 8px; + width: 0%; + height: 20px; + background: #0060df; + color: white; + font-weight: bold; + line-height: 20px; + padding: 0 8px 0 0; + text-align: right; + border-radius: 8px; + overflow: visible; + white-space: nowrap; +} + +.livePreview{ + position: absolute; + z-index: 300; + background-color: white; + margin: -4px; +} + +.livePreview img{ + object-fit: contain; + width: 100%; + height: 100%; } #lightboxModal{ @@ -450,23 +466,25 @@ input[type="range"]{ display:none } -#txt2img_interrupt, #img2img_interrupt{ - position: absolute; - width: 50%; - height: 72px; - background: #b4c0cc; - border-radius: 0px; - display: none; +#txt2img_generate_box, #img2img_generate_box{ + position: relative; +} + +#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{ + position: absolute; + width: 50%; + height: 100%; + background: #b4c0cc; + display: none; } +#txt2img_interrupt, #img2img_interrupt{ + right: 0; + border-radius: 0 0.5rem 0.5rem 0; +} #txt2img_skip, #img2img_skip{ - position: absolute; - width: 50%; - right: 0px; - height: 72px; - background: #b4c0cc; - border-radius: 0px; - display: none; + left: 0; + border-radius: 0.5rem 0 0 0.5rem; } .red { diff --git a/webui.py b/webui.py index 1fff80da..4624fe18 100644 --- a/webui.py +++ b/webui.py @@ -34,6 +34,7 @@ import modules.sd_vae import modules.txt2img import modules.script_callbacks import modules.textual_inversion.textual_inversion +import modules.progress import modules.ui from modules import modelloader @@ -181,6 +182,8 @@ def webui(): app.add_middleware(GZipMiddleware, minimum_size=1000) + modules.progress.setup_progress_api(app) + if launch_api: create_api(app) -- cgit v1.2.3 From 924e222004ab54273806c5f2ca7a0e7cfa76ad83 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 18 Jan 2023 23:04:24 +0300 Subject: add option to show/hide warnings removed hiding warnings from LDSR fixed/reworked few places that produced warnings --- extensions-builtin/LDSR/ldsr_model_arch.py | 3 -- javascript/localization.js | 2 +- modules/hypernetworks/hypernetwork.py | 7 ++++- modules/sd_hijack.py | 8 ------ modules/sd_hijack_checkpoint.py | 38 +++++++++++++++++++++++++- modules/shared.py | 1 + modules/textual_inversion/textual_inversion.py | 6 +++- modules/ui.py | 31 ++++++++++++--------- scripts/prompts_from_file.py | 2 +- style.css | 5 ++-- 10 files changed, 71 insertions(+), 32 deletions(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index 0ad49f4e..bc11cc6e 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -1,7 +1,6 @@ import os import gc import time -import warnings import numpy as np import torch @@ -15,8 +14,6 @@ from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap from modules import shared, sd_hijack -warnings.filterwarnings("ignore", category=UserWarning) - cached_ldsr_model: torch.nn.Module = None diff --git a/javascript/localization.js b/javascript/localization.js index bf9e1506..1a5a1dbb 100644 --- a/javascript/localization.js +++ b/javascript/localization.js @@ -11,7 +11,7 @@ ignore_ids_for_localization={ train_embedding: 'OPTION', train_hypernetwork: 'OPTION', txt2img_styles: 'OPTION', - img2img_styles 'OPTION', + img2img_styles: 'OPTION', setting_random_artist_categories: 'SPAN', setting_face_restoration_model: 'SPAN', setting_realesrgan_enabled_models: 'SPAN', diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index c963fc40..74e78582 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -12,7 +12,7 @@ import torch import tqdm from einops import rearrange, repeat from ldm.util import default -from modules import devices, processing, sd_models, shared, sd_samplers, hashes +from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint from modules.textual_inversion import textual_inversion, logging from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum @@ -575,6 +575,8 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi pbar = tqdm.tqdm(total=steps - initial_step) try: + sd_hijack_checkpoint.add() + for i in range((steps-initial_step) * gradient_step): if scheduler.finished: break @@ -724,6 +726,9 @@ Last saved image: {html.escape(last_saved_image)}
pbar.close() hypernetwork.eval() #report_statistics(loss_dict) + sd_hijack_checkpoint.remove() + + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') hypernetwork.optimizer_name = optimizer_name diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 6b0d95af..870eba88 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -69,12 +69,6 @@ def undo_optimizations(): ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward -def fix_checkpoint(): - ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward - ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward - ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward - - class StableDiffusionModelHijack: fixes = None comments = [] @@ -106,8 +100,6 @@ class StableDiffusionModelHijack: self.optimization_method = apply_optimizations() self.clip = m.cond_stage_model - - fix_checkpoint() def flatten(el): flattened = [flatten(children) for children in el.children()] diff --git a/modules/sd_hijack_checkpoint.py b/modules/sd_hijack_checkpoint.py index 5712972f..2604d969 100644 --- a/modules/sd_hijack_checkpoint.py +++ b/modules/sd_hijack_checkpoint.py @@ -1,10 +1,46 @@ from torch.utils.checkpoint import checkpoint +import ldm.modules.attention +import ldm.modules.diffusionmodules.openaimodel + + def BasicTransformerBlock_forward(self, x, context=None): return checkpoint(self._forward, x, context) + def AttentionBlock_forward(self, x): return checkpoint(self._forward, x) + def ResBlock_forward(self, x, emb): - return checkpoint(self._forward, x, emb) \ No newline at end of file + return checkpoint(self._forward, x, emb) + + +stored = [] + + +def add(): + if len(stored) != 0: + return + + stored.extend([ + ldm.modules.attention.BasicTransformerBlock.forward, + ldm.modules.diffusionmodules.openaimodel.ResBlock.forward, + ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward + ]) + + ldm.modules.attention.BasicTransformerBlock.forward = BasicTransformerBlock_forward + ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = ResBlock_forward + ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = AttentionBlock_forward + + +def remove(): + if len(stored) == 0: + return + + ldm.modules.attention.BasicTransformerBlock.forward = stored[0] + ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = stored[1] + ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = stored[2] + + stored.clear() + diff --git a/modules/shared.py b/modules/shared.py index a708f23c..ddb97f99 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -369,6 +369,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration" })) options_templates.update(options_section(('system', "System"), { + "show_warnings": OptionInfo(False, "Show warnings in console."), "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"), "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."), diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 7e4a6d24..5a7be422 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -15,7 +15,7 @@ import numpy as np from PIL import Image, PngImagePlugin from torch.utils.tensorboard import SummaryWriter -from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers +from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint import modules.textual_inversion.dataset from modules.textual_inversion.learn_schedule import LearnRateScheduler @@ -452,6 +452,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st pbar = tqdm.tqdm(total=steps - initial_step) try: + sd_hijack_checkpoint.add() + for i in range((steps-initial_step) * gradient_step): if scheduler.finished: break @@ -617,9 +619,11 @@ Last saved image: {html.escape(last_saved_image)}
pbar.close() shared.sd_model.first_stage_model.to(devices.device) shared.parallel_processing_allowed = old_parallel_processing_allowed + sd_hijack_checkpoint.remove() return embedding, filename + def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True): old_embedding_name = embedding.name old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None diff --git a/modules/ui.py b/modules/ui.py index 6d70a795..25818fb0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -11,6 +11,7 @@ import tempfile import time import traceback from functools import partial, reduce +import warnings import gradio as gr import gradio.routes @@ -41,6 +42,8 @@ from modules.textual_inversion import textual_inversion import modules.hypernetworks.ui from modules.generation_parameters_copypaste import image_from_url_text +warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning) + # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() mimetypes.add_type('application/javascript', '.js') @@ -417,17 +420,16 @@ def apply_setting(key, value): return value -def update_generation_info(args): - generation_info, html_info, img_index = args +def update_generation_info(generation_info, html_info, img_index): try: generation_info = json.loads(generation_info) if img_index < 0 or img_index >= len(generation_info["infotexts"]): - return html_info - return plaintext_to_html(generation_info["infotexts"][img_index]) + return html_info, gr.update() + return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update() except Exception: pass # if the json parse or anything else fails, just return the old html_info - return html_info + return html_info, gr.update() def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): @@ -508,10 +510,9 @@ Requested path was: {f} generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button") generation_info_button.click( fn=update_generation_info, - _js="(x, y) => [x, y, selected_gallery_index()]", - inputs=[generation_info, html_info], - outputs=[html_info], - preprocess=False + _js="function(x, y, z){ console.log(x, y, z); return [x, y, selected_gallery_index()] }", + inputs=[generation_info, html_info, html_info], + outputs=[html_info, html_info], ) save.click( @@ -526,7 +527,8 @@ Requested path was: {f} outputs=[ download_files, html_log, - ] + ], + show_progress=False, ) save_zip.click( @@ -588,7 +590,7 @@ def create_ui(): txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) - txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False) + txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False) with gr.Row().style(equal_height=False): with gr.Column(variant='compact', elem_id="txt2img_settings"): @@ -768,7 +770,7 @@ def create_ui(): with gr.Blocks(analytics_enabled=False) as img2img_interface: img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True) - img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False) + img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False) with FormRow().style(equal_height=False): with gr.Column(variant='compact', elem_id="img2img_settings"): @@ -1768,7 +1770,10 @@ def create_ui(): if saved_value is None: ui_settings[key] = getattr(obj, field) elif condition and not condition(saved_value): - print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') + pass + + # this warning is generally not useful; + # print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.') else: setattr(obj, field, saved_value) if init_field is not None: diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py index f3e711d7..76dc5778 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -116,7 +116,7 @@ class Script(scripts.Script): checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch")) prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt")) - file = gr.File(label="Upload prompt inputs", type='bytes', elem_id=self.elem_id("file")) + file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file")) file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt]) diff --git a/style.css b/style.css index 61279a19..0845519a 100644 --- a/style.css +++ b/style.css @@ -299,9 +299,8 @@ input[type="range"]{ } /* more gradio's garbage cleanup */ -.min-h-\[4rem\] { - min-height: unset !important; -} +.min-h-\[4rem\] { min-height: unset !important; } +.min-h-\[6rem\] { min-height: unset !important; } .progressDiv{ position: absolute; -- cgit v1.2.3 From 40ff6db5325fc34ad4fa35e80cb1e7768d9f7e75 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 21 Jan 2023 08:36:07 +0300 Subject: extra networks UI rework of hypernets: rather than via settings, hypernets are added directly to prompt as --- html/card-no-preview.png | Bin 0 -> 84440 bytes html/extra-networks-card.html | 11 ++ html/extra-networks-no-cards.html | 8 ++ javascript/extraNetworks.js | 60 ++++++++ javascript/hints.js | 2 + javascript/ui.js | 9 +- modules/api/api.py | 7 +- modules/extra_networks.py | 147 +++++++++++++++++++ modules/extra_networks_hypernet.py | 21 +++ modules/generation_parameters_copypaste.py | 12 +- modules/hypernetworks/hypernetwork.py | 107 +++++++++----- modules/hypernetworks/ui.py | 5 +- modules/processing.py | 24 ++-- modules/sd_hijack_optimizations.py | 10 +- modules/shared.py | 21 ++- modules/textual_inversion/textual_inversion.py | 2 + modules/ui.py | 50 ++++--- modules/ui_components.py | 10 ++ modules/ui_extra_networks.py | 149 +++++++++++++++++++ modules/ui_extra_networks_hypernets.py | 34 +++++ modules/ui_extra_networks_textual_inversion.py | 32 +++++ script.js | 13 +- scripts/xy_grid.py | 29 ---- style.css | 190 +++++++++++++------------ webui.py | 26 +++- 25 files changed, 765 insertions(+), 214 deletions(-) create mode 100644 html/card-no-preview.png create mode 100644 html/extra-networks-card.html create mode 100644 html/extra-networks-no-cards.html create mode 100644 javascript/extraNetworks.js create mode 100644 modules/extra_networks.py create mode 100644 modules/extra_networks_hypernet.py create mode 100644 modules/ui_extra_networks.py create mode 100644 modules/ui_extra_networks_hypernets.py create mode 100644 modules/ui_extra_networks_textual_inversion.py (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/html/card-no-preview.png b/html/card-no-preview.png new file mode 100644 index 00000000..e2beb269 Binary files /dev/null and b/html/card-no-preview.png differ diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html new file mode 100644 index 00000000..7314b063 --- /dev/null +++ b/html/extra-networks-card.html @@ -0,0 +1,11 @@ +
+
+
+ +
+ {name} +
+
+ diff --git a/html/extra-networks-no-cards.html b/html/extra-networks-no-cards.html new file mode 100644 index 00000000..389358d6 --- /dev/null +++ b/html/extra-networks-no-cards.html @@ -0,0 +1,8 @@ +
+

Nothing here. Add some content to the following directories:

+ +
    +{dirs} +
+
+ diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js new file mode 100644 index 00000000..71e522d1 --- /dev/null +++ b/javascript/extraNetworks.js @@ -0,0 +1,60 @@ + +function setupExtraNetworksForTab(tabname){ + gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks') + + gradioApp().querySelector('#'+tabname+'_extra_tabs > div').appendChild(gradioApp().getElementById(tabname+'_extra_refresh')) + gradioApp().querySelector('#'+tabname+'_extra_tabs > div').appendChild(gradioApp().getElementById(tabname+'_extra_close')) +} + +var activePromptTextarea = null; +var activePositivePromptTextarea = null; + +function setupExtraNetworks(){ + setupExtraNetworksForTab('txt2img') + setupExtraNetworksForTab('img2img') + + function registerPrompt(id, isNegative){ + var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); + + if (activePromptTextarea == null){ + activePromptTextarea = textarea + } + if (activePositivePromptTextarea == null && ! isNegative){ + activePositivePromptTextarea = textarea + } + + textarea.addEventListener("focus", function(){ + activePromptTextarea = textarea; + if(! isNegative) activePositivePromptTextarea = textarea; + }); + } + + registerPrompt('txt2img_prompt') + registerPrompt('txt2img_neg_prompt', true) + registerPrompt('img2img_prompt') + registerPrompt('img2img_neg_prompt', true) +} + +onUiLoaded(setupExtraNetworks) + +function cardClicked(textToAdd, allowNegativePrompt){ + textarea = allowNegativePrompt ? activePromptTextarea : activePositivePromptTextarea + + textarea.value = textarea.value + " " + textToAdd + updateInput(textarea) + + return false +} + +function saveCardPreview(event, tabname, filename){ + textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea') + button = gradioApp().getElementById(tabname + '_save_preview') + + textarea.value = filename + updateInput(textarea) + + button.click() + + event.stopPropagation() + event.preventDefault() +} diff --git a/javascript/hints.js b/javascript/hints.js index e746e20d..f4079f96 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -21,6 +21,8 @@ titles = { "\U0001F5D1": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", "\u{1f4d2}": "Paste available values into the field", + "\u{1f3b4}": "Show extra networks", + "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", diff --git a/javascript/ui.js b/javascript/ui.js index 3ba90ca8..a7e75439 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -196,8 +196,6 @@ function confirm_clear_prompt(prompt, negative_prompt) { return [prompt, negative_prompt] } - - opts = {} onUiUpdate(function(){ if(Object.keys(opts).length != 0) return; @@ -239,11 +237,14 @@ onUiUpdate(function(){ return } + prompt.parentElement.insertBefore(counter, prompt) counter.classList.add("token-counter") prompt.parentElement.style.position = "relative" - textarea.addEventListener("input", () => update_token_counter(id_button)); + textarea.addEventListener("input", function(){ + update_token_counter(id_button); + }); } registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button') @@ -261,10 +262,8 @@ onUiUpdate(function(){ }) } } - }) - onOptionsChanged(function(){ elem = gradioApp().getElementById('sd_checkpoint_hash') sd_checkpoint_hash = opts.sd_checkpoint_hash || "" diff --git a/modules/api/api.py b/modules/api/api.py index 9814bbc2..2c371e6e 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -480,7 +480,7 @@ class Api: def train_hypernetwork(self, args: dict): try: shared.state.begin() - initial_hypernetwork = shared.loaded_hypernetwork + shared.loaded_hypernetworks = [] apply_optimizations = shared.opts.training_xattention_optimizations error = None filename = '' @@ -491,16 +491,15 @@ class Api: except Exception as e: error = e finally: - shared.loaded_hypernetwork = initial_hypernetwork shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) if not apply_optimizations: sd_hijack.apply_optimizations() shared.state.end() - return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error)) + return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error)) except AssertionError as msg: shared.state.end() - return TrainResponse(info = "train embedding error: {error}".format(error = error)) + return TrainResponse(info="train embedding error: {error}".format(error=error)) def get_memory(self): try: diff --git a/modules/extra_networks.py b/modules/extra_networks.py new file mode 100644 index 00000000..1978673d --- /dev/null +++ b/modules/extra_networks.py @@ -0,0 +1,147 @@ +import re +from collections import defaultdict + +from modules import errors + +extra_network_registry = {} + + +def initialize(): + extra_network_registry.clear() + + +def register_extra_network(extra_network): + extra_network_registry[extra_network.name] = extra_network + + +class ExtraNetworkParams: + def __init__(self, items=None): + self.items = items or [] + + +class ExtraNetwork: + def __init__(self, name): + self.name = name + + def activate(self, p, params_list): + """ + Called by processing on every run. Whatever the extra network is meant to do should be activated here. + Passes arguments related to this extra network in params_list. + User passes arguments by specifying this in his prompt: + + + + Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments + separated by colon. + + Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list - + in this case, all effects of this extra networks should be disabled. + + Can be called multiple times before deactivate() - each new call should override the previous call completely. + + For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is: + + > "1girl, " + + params_list will be: + + [ + ExtraNetworkParams(items=["agm", "1.1"]), + ExtraNetworkParams(items=["ray"]) + ] + + """ + raise NotImplementedError + + def deactivate(self, p): + """ + Called at the end of processing for housekeeping. No need to do anything here. + """ + + raise NotImplementedError + + +def activate(p, extra_network_data): + """call activate for extra networks in extra_network_data in specified order, then call + activate for all remaining registered networks with an empty argument list""" + + for extra_network_name, extra_network_args in extra_network_data.items(): + extra_network = extra_network_registry.get(extra_network_name, None) + if extra_network is None: + print(f"Skipping unknown extra network: {extra_network_name}") + continue + + try: + extra_network.activate(p, extra_network_args) + except Exception as e: + errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}") + + for extra_network_name, extra_network in extra_network_registry.items(): + args = extra_network_data.get(extra_network_name, None) + if args is not None: + continue + + try: + extra_network.activate(p, []) + except Exception as e: + errors.display(e, f"activating extra network {extra_network_name}") + + +def deactivate(p, extra_network_data): + """call deactivate for extra networks in extra_network_data in specified order, then call + deactivate for all remaining registered networks""" + + for extra_network_name, extra_network_args in extra_network_data.items(): + extra_network = extra_network_registry.get(extra_network_name, None) + if extra_network is None: + continue + + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating extra network {extra_network_name}") + + for extra_network_name, extra_network in extra_network_registry.items(): + args = extra_network_data.get(extra_network_name, None) + if args is not None: + continue + + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating unmentioned extra network {extra_network_name}") + + +re_extra_net = re.compile(r"<(\w+):([^>]+)>") + + +def parse_prompt(prompt): + res = defaultdict(list) + + def found(m): + name = m.group(1) + args = m.group(2) + + res[name].append(ExtraNetworkParams(items=args.split(":"))) + + return "" + + prompt = re.sub(re_extra_net, found, prompt) + + return prompt, res + + +def parse_prompts(prompts): + res = [] + extra_data = None + + for prompt in prompts: + updated_prompt, parsed_extra_data = parse_prompt(prompt) + + if extra_data is None: + extra_data = parsed_extra_data + + res.append(updated_prompt) + + return res, extra_data + diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py new file mode 100644 index 00000000..6a0c4ba8 --- /dev/null +++ b/modules/extra_networks_hypernet.py @@ -0,0 +1,21 @@ +from modules import extra_networks +from modules.hypernetworks import hypernetwork + + +class ExtraNetworkHypernet(extra_networks.ExtraNetwork): + def __init__(self): + super().__init__('hypernet') + + def activate(self, p, params_list): + names = [] + multipliers = [] + for params in params_list: + assert len(params.items) > 0 + + names.append(params.items[0]) + multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0) + + hypernetwork.load_hypernetworks(names, multipliers) + + def deactivate(p, self): + pass diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index a381ff59..46e12dc6 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -79,8 +79,6 @@ def integrate_settings_paste_fields(component_dict): from modules import ui settings_map = { - 'sd_hypernetwork': 'Hypernet', - 'sd_hypernetwork_strength': 'Hypernet strength', 'CLIP_stop_at_last_layers': 'Clip skip', 'inpainting_mask_weight': 'Conditional mask weight', 'sd_model_checkpoint': 'Model hash', @@ -275,13 +273,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "Clip skip" not in res: res["Clip skip"] = "1" - if "Hypernet strength" not in res: - res["Hypernet strength"] = "1" - - if "Hypernet" in res: - hypernet_name = res["Hypernet"] - hypernet_hash = res.get("Hypernet hash", None) - res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash) + hypernet = res.get("Hypernet", None) + if hypernet is not None: + res["Prompt"] += f"""""" if "Hires resize-1" not in res: res["Hires resize-1"] = 0 diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 74e78582..80a47c79 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -25,7 +25,6 @@ from statistics import stdev, mean optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} class HypernetworkModule(torch.nn.Module): - multiplier = 1.0 activation_dict = { "linear": torch.nn.Identity, "relu": torch.nn.ReLU, @@ -41,6 +40,8 @@ class HypernetworkModule(torch.nn.Module): add_layer_norm=False, activate_output=False, dropout_structure=None): super().__init__() + self.multiplier = 1.0 + assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" @@ -115,7 +116,7 @@ class HypernetworkModule(torch.nn.Module): state_dict[to] = x def forward(self, x): - return x + self.linear(x) * (HypernetworkModule.multiplier if not self.training else 1) + return x + self.linear(x) * (self.multiplier if not self.training else 1) def trainables(self): layer_structure = [] @@ -125,9 +126,6 @@ class HypernetworkModule(torch.nn.Module): return layer_structure -def apply_strength(value=None): - HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength - #param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check. def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout): if layer_structure is None: @@ -192,6 +190,20 @@ class Hypernetwork: for param in layer.parameters(): param.requires_grad = mode + def to(self, device): + for k, layers in self.layers.items(): + for layer in layers: + layer.to(device) + + return self + + def set_multiplier(self, multiplier): + for k, layers in self.layers.items(): + for layer in layers: + layer.multiplier = multiplier + + return self + def eval(self): for k, layers in self.layers.items(): for layer in layers: @@ -269,11 +281,13 @@ class Hypernetwork: self.optimizer_state_dict = None if self.optimizer_state_dict: self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') - print("Loaded existing optimizer from checkpoint") - print(f"Optimizer name is {self.optimizer_name}") + if shared.opts.print_hypernet_extra: + print("Loaded existing optimizer from checkpoint") + print(f"Optimizer name is {self.optimizer_name}") else: self.optimizer_name = "AdamW" - print("No saved optimizer exists in checkpoint") + if shared.opts.print_hypernet_extra: + print("No saved optimizer exists in checkpoint") for size, sd in state_dict.items(): if type(size) == int: @@ -306,23 +320,43 @@ def list_hypernetworks(path): return res -def load_hypernetwork(filename): - path = shared.hypernetworks.get(filename, None) - # Prevent any file named "None.pt" from being loaded. - if path is not None and filename != "None": - print(f"Loading hypernetwork {filename}") - try: - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) +def load_hypernetwork(name): + path = shared.hypernetworks.get(name, None) - except Exception: - print(f"Error loading hypernetwork {path}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - else: - if shared.loaded_hypernetwork is not None: - print("Unloading hypernetwork") + if path is None: + return None + + hypernetwork = Hypernetwork() + + try: + hypernetwork.load(path) + except Exception: + print(f"Error loading hypernetwork {path}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + return None + + return hypernetwork + + +def load_hypernetworks(names, multipliers=None): + already_loaded = {} + + for hypernetwork in shared.loaded_hypernetworks: + if hypernetwork.name in names: + already_loaded[hypernetwork.name] = hypernetwork - shared.loaded_hypernetwork = None + shared.loaded_hypernetworks.clear() + + for i, name in enumerate(names): + hypernetwork = already_loaded.get(name, None) + if hypernetwork is None: + hypernetwork = load_hypernetwork(name) + + if hypernetwork is None: + continue + + hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0) + shared.loaded_hypernetworks.append(hypernetwork) def find_closest_hypernetwork_name(search: str): @@ -336,18 +370,27 @@ def find_closest_hypernetwork_name(search: str): return applicable[0] -def apply_hypernetwork(hypernetwork, context, layer=None): - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) +def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None): + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None) if hypernetwork_layers is None: - return context, context + return context_k, context_v if layer is not None: layer.hyper_k = hypernetwork_layers[0] layer.hyper_v = hypernetwork_layers[1] - context_k = hypernetwork_layers[0](context) - context_v = hypernetwork_layers[1](context) + context_k = hypernetwork_layers[0](context_k) + context_v = hypernetwork_layers[1](context_v) + return context_k, context_v + + +def apply_hypernetworks(hypernetworks, context, layer=None): + context_k = context + context_v = context + for hypernetwork in hypernetworks: + context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer) + return context_k, context_v @@ -357,7 +400,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) - context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) + context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self) k = self.to_k(context_k) v = self.to_v(context_v) @@ -464,8 +507,9 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi template_file = template_file.path path = shared.hypernetworks.get(hypernetwork_name, None) - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) + hypernetwork = Hypernetwork() + hypernetwork.load(path) + shared.loaded_hypernetworks = [hypernetwork] shared.state.job = "train-hypernetwork" shared.state.textinfo = "Initializing hypernetwork training..." @@ -489,7 +533,6 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi else: images_dir = None - hypernetwork = shared.loaded_hypernetwork checkpoint = sd_models.select_checkpoint() initial_step = hypernetwork.step or 0 diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 81e3f519..76599f5a 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,6 +9,7 @@ from modules import devices, sd_hijack, shared not_available = ["hardswish", "multiheadattention"] keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available) + def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure) @@ -16,8 +17,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, def train_hypernetwork(*args): - - initial_hypernetwork = shared.loaded_hypernetwork + shared.loaded_hypernetworks = [] assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible' @@ -34,7 +34,6 @@ Hypernetwork saved to {html.escape(filename)} except Exception: 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/processing.py b/modules/processing.py index a3e9f709..b5deeacf 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -13,7 +13,7 @@ from skimage import exposure from typing import Any, Dict, List, Optional import modules.sd_hijack -from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks +from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -438,9 +438,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), - "Hypernet hash": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.shorthash()), - "Hypernet strength": (None if shared.loaded_hypernetwork is None or shared.opts.sd_hypernetwork_strength >= 1 else shared.opts.sd_hypernetwork_strength), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), @@ -468,14 +465,12 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try: for k, v in p.override_settings.items(): setattr(opts, k, v) - if k == 'sd_hypernetwork': - shared.reload_hypernetworks() # make onchange call for changing hypernet if k == 'sd_model_checkpoint': - sd_models.reload_model_weights() # make onchange call for changing SD model + sd_models.reload_model_weights() if k == 'sd_vae': - sd_vae.reload_vae_weights() # make onchange call for changing VAE + sd_vae.reload_vae_weights() res = process_images_inner(p) @@ -484,9 +479,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if p.override_settings_restore_afterwards: for k, v in stored_opts.items(): setattr(opts, k, v) - if k == 'sd_hypernetwork': shared.reload_hypernetworks() - if k == 'sd_model_checkpoint': sd_models.reload_model_weights() - if k == 'sd_vae': sd_vae.reload_vae_weights() + if k == 'sd_model_checkpoint': + sd_models.reload_model_weights() + + if k == 'sd_vae': + sd_vae.reload_vae_weights() return res @@ -564,10 +561,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: cache[0] = (required_prompts, steps) return cache[1] + p.all_prompts, extra_network_data = extra_networks.parse_prompts(p.all_prompts) + with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): p.init(p.all_prompts, p.all_seeds, p.all_subseeds) + extra_networks.activate(p, extra_network_data) + with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file: processed = Processed(p, [], p.seed, "") file.write(processed.infotext(p, 0)) @@ -681,6 +682,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if opts.grid_save: images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) + extra_networks.deactivate(p, extra_network_data) devices.torch_gc() res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts) diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index cdc63ed7..4fa54329 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -44,7 +44,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) del context, context_k, context_v, x @@ -78,7 +78,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) @@ -203,7 +203,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) - context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context) k = self.to_k(context_k) * self.scale v = self.to_v(context_v) del context, context_k, context_v, x @@ -225,7 +225,7 @@ def sub_quad_attention_forward(self, x, context=None, mask=None): q = self.to_q(x) context = default(context, x) - context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context) k = self.to_k(context_k) v = self.to_v(context_v) del context, context_k, context_v, x @@ -284,7 +284,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x) context = default(context, x) - context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context) + context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context) k_in = self.to_k(context_k) v_in = self.to_v(context_v) diff --git a/modules/shared.py b/modules/shared.py index 2f366454..c0e11f18 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -23,6 +23,7 @@ demo = None sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml") sd_model_file = os.path.join(script_path, 'model.ckpt') default_sd_model_file = sd_model_file + parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) @@ -145,7 +146,7 @@ config_filename = cmd_opts.ui_settings_file os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = {} -loaded_hypernetwork = None +loaded_hypernetworks = [] def reload_hypernetworks(): @@ -153,8 +154,6 @@ def reload_hypernetworks(): global hypernetworks hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) - hypernetwork.load_hypernetwork(opts.sd_hypernetwork) - class State: @@ -399,8 +398,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list), "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"), - "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), - "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), @@ -661,3 +658,17 @@ mem_mon.start() def listfiles(dirname): filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")] return [file for file in filenames if os.path.isfile(file)] + + +def html_path(filename): + return os.path.join(script_path, "html", filename) + + +def html(filename): + path = html_path(filename) + + if os.path.exists(path): + with open(path, encoding="utf8") as file: + return file.read() + + return "" diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 5a7be422..4e90f690 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -50,6 +50,7 @@ class Embedding: self.sd_checkpoint = None self.sd_checkpoint_name = None self.optimizer_state_dict = None + self.filename = None def save(self, filename): embedding_data = { @@ -182,6 +183,7 @@ class EmbeddingDatabase: embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) embedding.vectors = vec.shape[0] embedding.shape = vec.shape[-1] + embedding.filename = path if self.expected_shape == -1 or self.expected_shape == embedding.shape: self.register_embedding(embedding, shared.sd_model) diff --git a/modules/ui.py b/modules/ui.py index 06c11848..d23b2b8e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -20,7 +20,7 @@ import numpy as np from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call -from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae +from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML from modules.paths import script_path @@ -90,6 +90,7 @@ refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 apply_style_symbol = '\U0001f4cb' # 📋 clear_prompt_symbol = '\U0001F5D1' # 🗑️ +extra_networks_symbol = '\U0001F3B4' # 🎴 def plaintext_to_html(text): @@ -324,6 +325,8 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: def update_token_counter(text, steps): try: + text, _ = extra_networks.parse_prompt(text) + _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text]) prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps) @@ -354,10 +357,10 @@ def create_toprow(is_img2img): negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)") with gr.Column(scale=1, elem_id="roll_col"): - paste = gr.Button(value=paste_symbol, elem_id="paste") - save_style = gr.Button(value=save_style_symbol, elem_id="style_create") - prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply") - clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt") + paste = ToolButton(value=paste_symbol, elem_id="paste") + clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt") + extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks") + token_counter = gr.HTML(value="", elem_id=f"{id_part}_token_counter") token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button") negative_token_counter = gr.HTML(value="", elem_id=f"{id_part}_negative_token_counter") @@ -395,11 +398,14 @@ def create_toprow(is_img2img): outputs=[], ) - with gr.Row(): + with gr.Row(elem_id=f"{id_part}_styles_row"): prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True) create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles") - return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button, negative_token_counter, negative_token_button + prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id="style_apply") + save_style = ToolButton(value=save_style_symbol, elem_id="style_create") + + return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button def setup_progressbar(*args, **kwargs): @@ -616,11 +622,15 @@ def create_ui(): modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False) with gr.Blocks(analytics_enabled=False) as txt2img_interface: - txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False) + txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False) dummy_component = gr.Label(visible=False) txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False) + with FormRow(variant='compact', elem_id="txt2img_extra_networks", visible=False) as extra_networks: + from modules import ui_extra_networks + extra_networks_ui = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'txt2img') + with gr.Row().style(equal_height=False): with gr.Column(variant='compact', elem_id="txt2img_settings"): for category in ordered_ui_categories(): @@ -794,14 +804,20 @@ def create_ui(): token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_prompt, steps], outputs=[token_counter]) negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) + ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery) + modules.scripts.scripts_current = modules.scripts.scripts_img2img modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True) with gr.Blocks(analytics_enabled=False) as img2img_interface: - img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True) + img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True) img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False) + with FormRow(variant='compact', elem_id="img2img_extra_networks", visible=False) as extra_networks: + from modules import ui_extra_networks + extra_networks_ui_img2img = ui_extra_networks.create_ui(extra_networks, extra_networks_button, 'img2img') + with FormRow().style(equal_height=False): with gr.Column(variant='compact', elem_id="img2img_settings"): copy_image_buttons = [] @@ -1064,6 +1080,8 @@ def create_ui(): token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter]) + ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery) + img2img_paste_fields = [ (img2img_prompt, "Prompt"), (img2img_negative_prompt, "Negative prompt"), @@ -1666,10 +1684,8 @@ def create_ui(): download_localization = gr.Button(value='Download localization template', elem_id="download_localization") reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies") - if os.path.exists("html/licenses.html"): - with open("html/licenses.html", encoding="utf8") as file: - with gr.TabItem("Licenses"): - gr.HTML(file.read(), elem_id="licenses") + with gr.TabItem("Licenses"): + gr.HTML(shared.html("licenses.html"), elem_id="licenses") gr.Button(value="Show all pages", elem_id="settings_show_all_pages") @@ -1756,11 +1772,9 @@ def create_ui(): if os.path.exists(os.path.join(script_path, "notification.mp3")): audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False) - if os.path.exists("html/footer.html"): - with open("html/footer.html", encoding="utf8") as file: - footer = file.read() - footer = footer.format(versions=versions_html()) - gr.HTML(footer, elem_id="footer") + footer = shared.html("footer.html") + footer = footer.format(versions=versions_html()) + gr.HTML(footer, elem_id="footer") text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False) settings_submit.click( diff --git a/modules/ui_components.py b/modules/ui_components.py index 97acff06..46324425 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -11,6 +11,16 @@ class ToolButton(gr.Button, gr.components.FormComponent): return "button" +class ToolButtonTop(gr.Button, gr.components.FormComponent): + """Small button with single emoji as text, with extra margin at top, fits inside gradio forms""" + + def __init__(self, **kwargs): + super().__init__(variant="tool-top", **kwargs) + + def get_block_name(self): + return "button" + + class FormRow(gr.Row, gr.components.FormComponent): """Same as gr.Row but fits inside gradio forms""" diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py new file mode 100644 index 00000000..253e90f7 --- /dev/null +++ b/modules/ui_extra_networks.py @@ -0,0 +1,149 @@ +import os.path + +from modules import shared +import gradio as gr +import json + +from modules.generation_parameters_copypaste import image_from_url_text + +extra_pages = [] + + +def register_page(page): + """registers extra networks page for the UI; recommend doing it in on_app_started() callback for extensions""" + + extra_pages.append(page) + + +class ExtraNetworksPage: + def __init__(self, title): + self.title = title + self.card_page = shared.html("extra-networks-card.html") + self.allow_negative_prompt = False + + def refresh(self): + pass + + def create_html(self, tabname): + items_html = '' + + for item in self.list_items(): + items_html += self.create_html_for_item(item, tabname) + + if items_html == '': + dirs = "".join([f"
  • {x}
    • " for x in self.allowed_directories_for_previews()]) + items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs) + + res = "
    " + items_html + "
    " + + return res + + def list_items(self): + raise NotImplementedError() + + def allowed_directories_for_previews(self): + return [] + + def create_html_for_item(self, item, tabname): + preview = item.get("preview", None) + + args = { + "preview_html": "style='background-image: url(" + json.dumps(preview) + ")'" if preview else '', + "prompt": json.dumps(item["prompt"]), + "tabname": json.dumps(tabname), + "local_preview": json.dumps(item["local_preview"]), + "name": item["name"], + "allow_negative_prompt": "true" if self.allow_negative_prompt else "false", + } + + return self.card_page.format(**args) + + +def intialize(): + extra_pages.clear() + + +class ExtraNetworksUi: + def __init__(self): + self.pages = None + self.stored_extra_pages = None + + self.button_save_preview = None + self.preview_target_filename = None + + self.tabname = None + + +def create_ui(container, button, tabname): + ui = ExtraNetworksUi() + ui.pages = [] + ui.stored_extra_pages = extra_pages.copy() + ui.tabname = tabname + + with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs: + button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh") + button_close = gr.Button('Close', elem_id=tabname+"_extra_close") + + for page in ui.stored_extra_pages: + with gr.Tab(page.title): + page_elem = gr.HTML(page.create_html(ui.tabname)) + ui.pages.append(page_elem) + + ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False) + ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False) + + button.click(fn=lambda: gr.update(visible=True), inputs=[], outputs=[container]) + button_close.click(fn=lambda: gr.update(visible=False), inputs=[], outputs=[container]) + + def refresh(): + res = [] + + for pg in ui.stored_extra_pages: + pg.refresh() + res.append(pg.create_html(ui.tabname)) + + return res + + button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages) + + return ui + + +def path_is_parent(parent_path, child_path): + parent_path = os.path.abspath(parent_path) + child_path = os.path.abspath(child_path) + + return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path]) + + +def setup_ui(ui, gallery): + def save_preview(index, images, filename): + if len(images) == 0: + print("There is no image in gallery to save as a preview.") + return [page.create_html(ui.tabname) for page in ui.stored_extra_pages] + + index = int(index) + index = 0 if index < 0 else index + index = len(images) - 1 if index >= len(images) else index + + img_info = images[index if index >= 0 else 0] + image = image_from_url_text(img_info) + + is_allowed = False + for extra_page in ui.stored_extra_pages: + if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]): + is_allowed = True + break + + assert is_allowed, f'writing to {filename} is not allowed' + + image.save(filename) + + return [page.create_html(ui.tabname) for page in ui.stored_extra_pages] + + ui.button_save_preview.click( + fn=save_preview, + _js="function(x, y, z){console.log(x, y, z); return [selected_gallery_index(), y, z]}", + inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename], + outputs=[*ui.pages] + ) diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py new file mode 100644 index 00000000..312dbaf0 --- /dev/null +++ b/modules/ui_extra_networks_hypernets.py @@ -0,0 +1,34 @@ +import os + +from modules import shared, ui_extra_networks + + +class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Hypernetworks') + + def refresh(self): + shared.reload_hypernetworks() + + def list_items(self): + for name, path in shared.hypernetworks.items(): + path, ext = os.path.splitext(path) + previews = [path + ".png", path + ".preview.png"] + + preview = None + for file in previews: + if os.path.isfile(file): + preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file)) + break + + yield { + "name": name, + "filename": path, + "preview": preview, + "prompt": f"", + "local_preview": path + ".png", + } + + def allowed_directories_for_previews(self): + return [shared.cmd_opts.hypernetwork_dir] + diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py new file mode 100644 index 00000000..e4a6e3bf --- /dev/null +++ b/modules/ui_extra_networks_textual_inversion.py @@ -0,0 +1,32 @@ +import os + +from modules import ui_extra_networks, sd_hijack + + +class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Textual Inversion') + self.allow_negative_prompt = True + + def refresh(self): + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) + + def list_items(self): + for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values(): + path, ext = os.path.splitext(embedding.filename) + preview_file = path + ".preview.png" + + preview = None + if os.path.isfile(preview_file): + preview = "./file=" + preview_file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(preview_file)) + + yield { + "name": embedding.name, + "filename": embedding.filename, + "preview": preview, + "prompt": embedding.name, + "local_preview": path + ".preview.png", + } + + def allowed_directories_for_previews(self): + return list(sd_hijack.model_hijack.embedding_db.embedding_dirs) diff --git a/script.js b/script.js index 3345e32b..97e0bfcf 100644 --- a/script.js +++ b/script.js @@ -13,6 +13,7 @@ function get_uiCurrentTabContent() { } uiUpdateCallbacks = [] +uiLoadedCallbacks = [] uiTabChangeCallbacks = [] optionsChangedCallbacks = [] let uiCurrentTab = null @@ -20,6 +21,9 @@ let uiCurrentTab = null function onUiUpdate(callback){ uiUpdateCallbacks.push(callback) } +function onUiLoaded(callback){ + uiLoadedCallbacks.push(callback) +} function onUiTabChange(callback){ uiTabChangeCallbacks.push(callback) } @@ -38,8 +42,15 @@ function executeCallbacks(queue, m) { queue.forEach(function(x){runCallback(x, m)}) } +var executedOnLoaded = false; + document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ + if(!executedOnLoaded && gradioApp().querySelector('#txt2img_prompt')){ + executedOnLoaded = true; + executeCallbacks(uiLoadedCallbacks); + } + executeCallbacks(uiUpdateCallbacks, m); const newTab = get_uiCurrentTab(); if ( newTab && ( newTab !== uiCurrentTab ) ) { @@ -53,7 +64,7 @@ document.addEventListener("DOMContentLoaded", function() { /** * Add a ctrl+enter as a shortcut to start a generation */ - document.addEventListener('keydown', function(e) { +document.addEventListener('keydown', function(e) { var handled = false; if (e.key !== undefined) { if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true; diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 6629f5d5..b1badec9 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -11,7 +11,6 @@ import modules.scripts as scripts import gradio as gr from modules import images, paths, sd_samplers, processing, sd_models, sd_vae -from modules.hypernetworks import hypernetwork from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -94,28 +93,6 @@ def confirm_checkpoints(p, xs): raise RuntimeError(f"Unknown checkpoint: {x}") -def apply_hypernetwork(p, x, xs): - if x.lower() in ["", "none"]: - name = None - else: - name = hypernetwork.find_closest_hypernetwork_name(x) - if not name: - raise RuntimeError(f"Unknown hypernetwork: {x}") - hypernetwork.load_hypernetwork(name) - - -def apply_hypernetwork_strength(p, x, xs): - hypernetwork.apply_strength(x) - - -def confirm_hypernetworks(p, xs): - for x in xs: - if x.lower() in ["", "none"]: - continue - if not hypernetwork.find_closest_hypernetwork_name(x): - raise RuntimeError(f"Unknown hypernetwork: {x}") - - def apply_clip_skip(p, x, xs): opts.data["CLIP_stop_at_last_layers"] = x @@ -208,8 +185,6 @@ axis_options = [ AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list), AxisOption("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]), AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)), - AxisOption("Hypernetwork", str, apply_hypernetwork, format_value=format_value, confirm=confirm_hypernetworks, cost=0.2, choices=lambda: list(shared.hypernetworks)), - AxisOption("Hypernet str.", float, apply_hypernetwork_strength), AxisOption("Sigma Churn", float, apply_field("s_churn")), AxisOption("Sigma min", float, apply_field("s_tmin")), AxisOption("Sigma max", float, apply_field("s_tmax")), @@ -291,7 +266,6 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_ class SharedSettingsStackHelper(object): def __enter__(self): self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers - self.hypernetwork = opts.sd_hypernetwork self.vae = opts.sd_vae def __exit__(self, exc_type, exc_value, tb): @@ -299,9 +273,6 @@ class SharedSettingsStackHelper(object): modules.sd_models.reload_model_weights() modules.sd_vae.reload_vae_weights() - hypernetwork.load_hypernetwork(self.hypernetwork) - hypernetwork.apply_strength() - opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers diff --git a/style.css b/style.css index 3a515ebd..5e8bc2ca 100644 --- a/style.css +++ b/style.css @@ -132,13 +132,6 @@ } #roll_col > button { - min-width: 2em; - min-height: 2em; - max-width: 2em; - max-height: 2em; - flex-grow: 0; - padding-left: 0.25em; - padding-right: 0.25em; margin: 0.1em 0; } @@ -146,9 +139,10 @@ min-width: 0 !important; max-width: 8em !important; margin-right: 1em; + gap: 0; } #interrogate, #deepbooru{ - margin: 0em 0.25em 0.9em 0.25em; + margin: 0em 0.25em 0.5em 0.25em; min-width: 8em; max-width: 8em; } @@ -157,8 +151,17 @@ min-width: 8em !important; } +#txt2img_styles_row, #img2img_styles_row{ + gap: 0.25em; + margin-top: 0.5em; +} + +#txt2img_styles_row > button, #img2img_styles_row > button{ + margin: 0; +} + #txt2img_styles, #img2img_styles{ - margin-top: 1em; + padding: 0; } #txt2img_styles ul, #img2img_styles ul{ @@ -635,17 +638,21 @@ canvas[key="mask"] { background-color: rgb(31 41 55 / var(--tw-bg-opacity)); } -.gr-button-tool{ +.gr-button-tool, .gr-button-tool-top{ max-width: 2.5em; min-width: 2.5em !important; height: 2.4em; - margin: 1.6em 0.7em 0.55em 0; } -#tab_modelmerger .gr-button-tool{ +.gr-button-tool{ margin: 0.6em 0em 0.55em 0; } +.gr-button-tool-top, #settings .gr-button-tool{ + margin: 1.6em 0.7em 0.55em 0; +} + + #modelmerger_results_container{ margin-top: 1em; overflow: visible; @@ -763,81 +770,88 @@ footer { line-height: 2.4em; } -/* The following handles localization for right-to-left (RTL) languages like Arabic. -The rtl media type will only be activated by the logic in javascript/localization.js. -If you change anything above, you need to make sure it is RTL compliant by just running -your changes through converters like https://cssjanus.github.io/ or https://rtlcss.com/. -Then, you will need to add the RTL counterpart only if needed in the rtl section below.*/ -@media rtl { - /* this part was added manually */ - :host { - direction: rtl; - } - select, .file-preview, .gr-text-input, .output-html:has(.performance), #ti_progress { - direction: ltr; - } - #script_list > label > select, - #x_type > label > select, - #y_type > label > select { - direction: rtl; - } - .gr-radio, .gr-checkbox{ - margin-left: 0.25em; - } +#txt2img_extra_networks, #img2img_extra_networks{ + margin-top: -1em; +} - /* automatically generated with few manual modifications */ - .performance .time { - margin-right: unset; - margin-left: 0; - } - .justify-center.overflow-x-scroll { - justify-content: right; - } - .justify-center.overflow-x-scroll button:first-of-type { - margin-left: unset; - margin-right: auto; - } - .justify-center.overflow-x-scroll button:last-of-type { - margin-right: unset; - margin-left: auto; - } - #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{ - margin-right: unset; - margin-left: 8em; - } - #txt2img_progressbar, #img2img_progressbar, #ti_progressbar{ - right: unset; - left: 0; - } - .progressDiv .progress{ - padding: 0 0 0 8px; - text-align: left; - } - #lightboxModal{ - left: unset; - right: 0; - } - .modalPrev, .modalNext{ - border-radius: 3px 0 0 3px; - } - .modalNext { - right: unset; - left: 0; - border-radius: 0 3px 3px 0; - } - #imageARPreview{ - left:unset; - right:0px; - } - #txt2img_skip, #img2img_skip{ - right: unset; - left: 0px; - } - #context-menu{ - box-shadow:-1px 1px 2px #CE6400; - } - .gr-box > div > div > input.gr-text-input{ - right: unset; - left: 0.5em; - } +.extra-networks > div > [id *= '_extra_']{ + margin: 0.3em; } + +.extra-network-cards .nocards{ + margin: 1.25em 0.5em 0.5em 0.5em; +} + +.extra-network-cards .nocards h1{ + font-size: 1.5em; + margin-bottom: 1em; +} + +.extra-network-cards .nocards li{ + margin-left: 0.5em; +} + +.extra-network-cards .card{ + display: inline-block; + margin: 0.5em; + width: 16em; + height: 24em; + box-shadow: 0 0 5px rgba(128, 128, 128, 0.5); + border-radius: 0.2em; + position: relative; + + background-size: auto 100%; + background-position: center; + overflow: hidden; + cursor: pointer; + + background-image: url('./file=html/card-no-preview.png') +} + +.extra-network-cards .card:hover{ + box-shadow: 0 0 2px 0.3em rgba(0, 128, 255, 0.35); +} + +.extra-network-cards .card .actions .additional{ + display: none; +} + +.extra-network-cards .card .actions{ + position: absolute; + bottom: 0; + left: 0; + right: 0; + padding: 0.5em; + color: white; + background: rgba(0,0,0,0.5); + box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5); + text-shadow: 0 0 0.2em black; +} + +.extra-network-cards .card .actions:hover{ + box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important; +} + +.extra-network-cards .card .actions .name{ + font-size: 1.7em; + font-weight: bold; + line-break: anywhere; +} + +.extra-network-cards .card .actions:hover .additional{ + display: block; +} + +.extra-network-cards .card ul{ + margin: 0.25em 0 0.75em 0.25em; + cursor: unset; +} + +.extra-network-cards .card ul a{ + cursor: pointer; +} + +.extra-network-cards .card ul a:hover{ + color: red; +} + diff --git a/webui.py b/webui.py index 865a7300..e8dd822a 100644 --- a/webui.py +++ b/webui.py @@ -9,16 +9,18 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.gzip import GZipMiddleware -from modules import import_hook, errors +from modules import import_hook, errors, extra_networks +from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call from modules.paths import script_path import torch + # Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors if ".dev" in torch.__version__ or "+git" in torch.__version__: torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0) -from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir +from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks import modules.codeformer_model as codeformer import modules.extras import modules.face_restoration @@ -84,10 +86,17 @@ def initialize(): shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights())) shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False) - shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: shared.reload_hypernetworks())) - shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength) shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed) + shared.reload_hypernetworks() + + ui_extra_networks.intialize() + ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion()) + ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks()) + + extra_networks.initialize() + extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet()) + if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None: try: @@ -209,6 +218,15 @@ def webui(): modules.sd_models.list_models() + shared.reload_hypernetworks() + + ui_extra_networks.intialize() + ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion()) + ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks()) + + extra_networks.initialize() + extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet()) + if __name__ == "__main__": if cmd_opts.nowebui: -- cgit v1.2.3 From e179b6098ac1b1ce9645fef5bd9fd0bc9b918f30 Mon Sep 17 00:00:00 2001 From: "Alex \"mcmonkey\" Goodwin" Date: Wed, 25 Jan 2023 08:48:40 -0800 Subject: allow symlinks in the textual inversion embeddings folder --- modules/textual_inversion/textual_inversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/textual_inversion/textual_inversion.py') diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 4e90f690..6cf00e65 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -194,7 +194,7 @@ class EmbeddingDatabase: if not os.path.isdir(embdir.path): return - for root, dirs, fns in os.walk(embdir.path): + for root, dirs, fns in os.walk(embdir.path, followlinks=True): for fn in fns: try: fullfn = os.path.join(root, fn) -- cgit v1.2.3