From 14c1c2b9351f16d43ba4e6b6c9062edad44a6bec Mon Sep 17 00:00:00 2001 From: Alexandre Simard Date: Wed, 19 Oct 2022 13:53:52 -0400 Subject: Show PB texts at same time and earlier For big tasks (1000+ steps), waiting 1 minute to see ETA is long and this changes it so the number of steps done plays a role in showing the text as well. --- modules/ui.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index a2dbd41e..0abd177a 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -261,14 +261,14 @@ def wrap_gradio_call(func, extra_outputs=None): return f -def calc_time_left(progress, threshold, label, force_display): +def calc_time_left(progress, threshold, label, force_display, showTime): 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 progress > 0.02) or force_display: + if (eta_relative > threshold and showTime) or force_display: if eta_relative > 3600: return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) elif eta_relative > 60: @@ -290,7 +290,10 @@ def check_progress_call(id_part): if shared.state.sampling_steps > 0: progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps - time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display ) + # Show progress percentage and time left at the same moment, and base it also on steps done + showPBText = progress >= 0.01 or shared.state.sampling_step >= 10 + + time_left = calc_time_left( progress, 1, " ETA: ", shared.state.time_left_force_display, showPBText ) if time_left != "": shared.state.time_left_force_display = True @@ -298,7 +301,7 @@ def check_progress_call(id_part): progressbar = "" if opts.show_progressbar: - progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if progress > 0.01 else ""}
""" + progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) -- cgit v1.2.1 From c4b5ca5778340b21288d84dfb8fe1d5773c886a8 Mon Sep 17 00:00:00 2001 From: Yuta Hayashibe Date: Thu, 27 Oct 2022 22:00:28 +0900 Subject: Truncate too long filename --- modules/images.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 7870b5b7..42363ed3 100644 --- a/modules/images.py +++ b/modules/images.py @@ -416,6 +416,14 @@ def get_next_sequence_number(path, basename): return result + 1 +def truncate_fullpath(full_path, encoding='utf-8'): + dir_name, full_name = os.path.split(full_path) + file_name, file_ext = os.path.splitext(full_name) + max_length = os.statvfs(dir_name).f_namemax + file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore') + return os.path.join(dir_name , file_name_truncated + file_ext) + + def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): """Save an image. @@ -456,7 +464,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i if save_to_dirs: dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /') - path = os.path.join(path, dirname) + path = truncate_fullpath(os.path.join(path, dirname)) os.makedirs(path, exist_ok=True) @@ -480,13 +488,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i fullfn = None for i in range(500): fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}" - fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}") + fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}")) if not os.path.exists(fullfn): break else: - fullfn = os.path.join(path, f"{file_decoration}.{extension}") + fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}")) else: - fullfn = os.path.join(path, f"{forced_filename}.{extension}") + fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}")) pnginfo = existing_info or {} if info is not None: -- cgit v1.2.1 From 2a25729623717cc499e873752d9f4ebebd1e1078 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Fri, 28 Oct 2022 09:44:56 +0700 Subject: Gradient clipping in train tab --- modules/hypernetworks/hypernetwork.py | 10 +++++++++- modules/ui.py | 7 +++++++ 2 files changed, 16 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 8113b35b..c5d60654 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -327,7 +327,7 @@ def report_statistics(loss_info:dict): -def train_hypernetwork(hypernetwork_name, 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): +def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, 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 @@ -384,6 +384,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if ititial_step > steps: return hypernetwork, filename + clip_grad_mode_value = clip_grad_mode == "value" + clip_grad_mode_norm = clip_grad_mode == "norm" + 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) @@ -426,6 +429,11 @@ 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_value) + elif clip_grad_mode_norm: + torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_value) + optimizer.step() if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): diff --git a/modules/ui.py b/modules/ui.py index 0a63e357..97de7da2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1313,6 +1313,9 @@ 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) @@ -1406,6 +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, @@ -1431,6 +1436,8 @@ 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 v1.2.1 From a133042c669f666763f5da0f4440abdc839db653 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Fri, 28 Oct 2022 10:01:46 +0700 Subject: Forgot to remove this from train_embedding --- modules/ui.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 97de7da2..ba5e92a7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1409,8 +1409,6 @@ 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 v1.2.1 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') 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 v1.2.1 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') 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 v1.2.1 From 840307f23738c38f7ac3ad636e53ccec66e71f8b Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 13:49:24 +0700 Subject: Change default clip grad value to 0.1 It still defaults to disabled. Ref for value: https://github.com/danielalcalde/stable-diffusion-webui/commit/732b15820a9bde9f47e075a6209c3d47d47acb08 --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 98f9565f..364953aa 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1256,7 +1256,7 @@ def create_ui(wrap_gradio_gpu_call): 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) + clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", 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") -- cgit v1.2.1 From 4123be632a98f70cda06e14c2f556f7ad38cd436 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 13:53:22 +0700 Subject: Fix merge conflicts --- modules/hypernetworks/hypernetwork.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 65a584bb..207808ee 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -373,6 +373,12 @@ 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_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)}..." with torch.autocast("cuda"): @@ -389,21 +395,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - last_saved_file = "" - last_saved_image = "" - forced_filename = "" - 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) weights = hypernetwork.weights() for weight in weights: -- cgit v1.2.1 From d5ea878b2aa117588d85287cbd8983aa52177df5 Mon Sep 17 00:00:00 2001 From: Muhammad Rizqi Nur Date: Mon, 31 Oct 2022 13:54:40 +0700 Subject: Fix merge conflicts --- modules/hypernetworks/hypernetwork.py | 5 ----- 1 file changed, 5 deletions(-) (limited to 'modules') diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 207808ee..2df38c70 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -395,11 +395,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log previous_mean_loss = 0 print("Mean loss of {} elements".format(size)) - ititial_step = hypernetwork.step or 0 - if ititial_step > steps: - return hypernetwork, filename - - weights = hypernetwork.weights() for weight in weights: weight.requires_grad = True -- cgit v1.2.1 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') 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 v1.2.1 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') 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 v1.2.1 From f23a822f1c9cb3bd2e8772c75af429e06515eaef Mon Sep 17 00:00:00 2001 From: Philpax Date: Sat, 24 Dec 2022 20:45:16 +1100 Subject: feat(api): include job_timestamp in progress --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 8ea3b441..f356dbf7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -171,6 +171,7 @@ class State: "interrupted": self.skipped, "job": self.job, "job_count": self.job_count, + "job_timestamp": self.job_timestamp, "job_no": self.job_no, "sampling_step": self.sampling_step, "sampling_steps": self.sampling_steps, -- cgit v1.2.1 From 5ba04f9ec050a66e918571f07e8863f157f05b44 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Wed, 21 Dec 2022 13:45:58 +0100 Subject: Attempting to solve slow loads for `safetensors`. Fixes #5893 --- modules/sd_models.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index ecdd91c5..cd938656 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -168,7 +168,10 @@ def get_state_dict_from_checkpoint(pl_sd): def read_state_dict(checkpoint_file, print_global_state=False, map_location=None): _, extension = os.path.splitext(checkpoint_file) if extension.lower() == ".safetensors": - pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location) + device = map_location or shared.weight_load_location + if device is None: + device = "cuda:0" if torch.cuda.is_available() else "cpu" + pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) else: pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location) -- cgit v1.2.1 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') 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 v1.2.1 From aaa4c2aacbb6523077334093c81bd475d757f7a1 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 09:45:16 -0500 Subject: add api logging --- modules/api/api.py | 24 +++++++++++++++++++++++- modules/shared.py | 1 + 2 files changed, 24 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 9c670f00..53135470 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,11 +1,12 @@ import base64 import io import time +import datetime import uvicorn from threading import Lock from io import BytesIO from gradio.processing_utils import decode_base64_to_file -from fastapi import APIRouter, Depends, FastAPI, HTTPException +from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response from fastapi.security import HTTPBasic, HTTPBasicCredentials from secrets import compare_digest @@ -67,6 +68,26 @@ def encode_pil_to_base64(image): bytes_data = output_bytes.getvalue() return base64.b64encode(bytes_data) +def init_api_middleware(app: FastAPI): + @app.middleware("http") + async def log_and_time(req: Request, call_next): + ts = time.time() + res: Response = await call_next(req) + duration = str(round(time.time() - ts, 4)) + res.headers["X-Process-Time"] = duration + if shared.cmd_opts.api_log: + print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format( + t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), + code = res.status_code, + ver = req.scope.get('http_version', '0.0'), + cli = req.scope.get('client', ('0:0.0.0', 0))[0], + prot = req.scope.get('scheme', 'err'), + method = req.scope.get('method', 'err'), + p = req.scope.get('path', 'err'), + duration = duration, + )) + return res + class Api: def __init__(self, app: FastAPI, queue_lock: Lock): @@ -78,6 +99,7 @@ class Api: self.router = APIRouter() self.app = app + init_api_middleware(self.app) self.queue_lock = queue_lock self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) diff --git a/modules/shared.py b/modules/shared.py index 23657a93..2a03d716 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -82,6 +82,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)") parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests") parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui") parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) -- cgit v1.2.1 From 1d9dc48efda2e8da6d13fc62e65500198a9b041c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 10:21:51 -0500 Subject: init job and add info to model merge --- modules/extras.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index 5e270250..7e222313 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -242,6 +242,9 @@ def run_pnginfo(image): def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format): + shared.state.begin() + shared.state.job = 'model-merge' + def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) @@ -263,8 +266,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam theta_func1, theta_func2 = theta_funcs[interp_method] if theta_func1 and not tertiary_model_info: + shared.state.textinfo = "Failed: Interpolation method requires a tertiary model." + shared.state.end() return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)] + shared.state.textinfo = f"Loading {secondary_model_info.filename}..." print(f"Loading {secondary_model_info.filename}...") theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu') @@ -281,6 +287,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam theta_1[key] = torch.zeros_like(theta_1[key]) del theta_2 + shared.state.textinfo = f"Loading {primary_model_info.filename}..." print(f"Loading {primary_model_info.filename}...") theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu') @@ -291,6 +298,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam a = theta_0[key] b = theta_1[key] + shared.state.textinfo = f'Merging layer {key}' # this enables merging an inpainting model (A) with another one (B); # where normal model would have 4 channels, for latenst space, inpainting model would # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9 @@ -303,8 +311,6 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier) result_is_inpainting_model = True else: - assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}' - theta_0[key] = theta_func2(a, b, multiplier) if save_as_half: @@ -332,6 +338,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam output_modelname = os.path.join(ckpt_dir, filename) + shared.state.textinfo = f"Saving to {output_modelname}..." print(f"Saving to {output_modelname}...") _, extension = os.path.splitext(output_modelname) @@ -343,4 +350,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam sd_models.list_models() print("Checkpoint saved.") + shared.state.textinfo = "Checkpoint saved to " + output_modelname + shared.state.end() + return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)] -- cgit v1.2.1 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') 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 v1.2.1 From cec209981ee988536c2521297baf9bc1b256005f Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 10:58:52 -0500 Subject: log only sdapi --- modules/api/api.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 53135470..78751c57 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -68,22 +68,23 @@ def encode_pil_to_base64(image): bytes_data = output_bytes.getvalue() return base64.b64encode(bytes_data) -def init_api_middleware(app: FastAPI): +def api_middleware(app: FastAPI): @app.middleware("http") async def log_and_time(req: Request, call_next): ts = time.time() res: Response = await call_next(req) duration = str(round(time.time() - ts, 4)) res.headers["X-Process-Time"] = duration - if shared.cmd_opts.api_log: - print('API {t} {code} {prot}/{ver} {method} {p} {cli} {duration}'.format( + endpoint = req.scope.get('path', 'err') + if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'): + print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format( t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), code = res.status_code, ver = req.scope.get('http_version', '0.0'), cli = req.scope.get('client', ('0:0.0.0', 0))[0], prot = req.scope.get('scheme', 'err'), method = req.scope.get('method', 'err'), - p = req.scope.get('path', 'err'), + endpoint = endpoint, duration = duration, )) return res -- cgit v1.2.1 From d8d206c1685d1e7027d4af82ed18d106f41d1cc4 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 3 Jan 2023 11:01:04 -0500 Subject: add state to interrogate --- modules/interrogate.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/interrogate.py b/modules/interrogate.py index 6f761c5a..738d8ff7 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -136,7 +136,8 @@ class InterrogateModels: def interrogate(self, pil_image): res = "" - + shared.state.begin() + shared.state.job = 'interrogate' try: if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -177,5 +178,6 @@ class InterrogateModels: res += "" self.unload() + shared.state.end() return res -- cgit v1.2.1 From 917b5bd8d0cd47c9dc241c1852ccd440a8c61668 Mon Sep 17 00:00:00 2001 From: Max Weber Date: Tue, 3 Jan 2023 18:19:56 -0700 Subject: ui: save dropdown sampling method to the ui-config --- modules/ui.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index d941cb5f..bfc93634 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -635,6 +635,7 @@ def create_sampler_and_steps_selection(choices, tabname): if opts.samplers_in_dropdown: with FormRow(elem_id=f"sampler_selection_{tabname}"): sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") + sampler_index.save_to_config = True steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20) else: with FormGroup(elem_id=f"sampler_selection_{tabname}"): -- cgit v1.2.1 From e5b7ee910e7bb88f08e8876b5732cb034c6fe529 Mon Sep 17 00:00:00 2001 From: MMaker Date: Wed, 4 Jan 2023 04:22:01 -0500 Subject: fix: Save full res of intermediate step --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index a172af0b..93e75ba6 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -705,7 +705,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return if not isinstance(image, Image.Image): - image = sd_samplers.sample_to_image(image, index) + image = sd_samplers.sample_to_image(image, index, approximation=0) images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") -- cgit v1.2.1 From 02d7abf5141431b9a3a8a189bb3136c71abd5e79 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 12:35:07 +0300 Subject: helpful error message when trying to load 2.0 without config failing to load model weights from settings won't break generation for currently loaded model anymore --- modules/errors.py | 25 +++++++++++++++++++++++-- modules/sd_models.py | 26 ++++++++++++++++++-------- modules/shared.py | 9 +++++++-- 3 files changed, 48 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/errors.py b/modules/errors.py index 372dc51a..a668c014 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -2,9 +2,30 @@ import sys import traceback +def print_error_explanation(message): + lines = message.strip().split("\n") + max_len = max([len(x) for x in lines]) + + print('=' * max_len, file=sys.stderr) + for line in lines: + print(line, file=sys.stderr) + print('=' * max_len, file=sys.stderr) + + +def display(e: Exception, task): + print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + message = str(e) + if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message: + print_error_explanation(""" +The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file. +See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this. + """) + + def run(code, task): try: code() except Exception as e: - print(f"{task}: {type(e).__name__}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + display(task, e) diff --git a/modules/sd_models.py b/modules/sd_models.py index b98b05fc..6846b74a 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -278,6 +278,7 @@ def enable_midas_autodownload(): midas.api.load_model = load_model_wrapper + def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() @@ -312,6 +313,7 @@ def load_model(checkpoint_info=None): sd_config.model.params.unet_config.params.use_fp16 = False sd_model = instantiate_from_config(sd_config.model) + load_model_weights(sd_model, checkpoint_info) if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -336,10 +338,12 @@ def load_model(checkpoint_info=None): def reload_model_weights(sd_model=None, info=None): from modules import lowvram, devices, sd_hijack checkpoint_info = info or select_checkpoint() - + if not sd_model: sd_model = shared.sd_model + current_checkpoint_info = sd_model.sd_checkpoint_info + if sd_model.sd_model_checkpoint == checkpoint_info.filename: return @@ -356,13 +360,19 @@ def reload_model_weights(sd_model=None, info=None): sd_hijack.model_hijack.undo_hijack(sd_model) - load_model_weights(sd_model, checkpoint_info) - - sd_hijack.model_hijack.hijack(sd_model) - script_callbacks.model_loaded_callback(sd_model) - - if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: - sd_model.to(devices.device) + try: + load_model_weights(sd_model, checkpoint_info) + except Exception as e: + print("Failed to load checkpoint, restoring previous") + load_model_weights(sd_model, current_checkpoint_info) + raise + finally: + sd_hijack.model_hijack.hijack(sd_model) + script_callbacks.model_loaded_callback(sd_model) + + if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: + sd_model.to(devices.device) print("Weights loaded.") + return sd_model diff --git a/modules/shared.py b/modules/shared.py index 23657a93..7588c47b 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -14,7 +14,7 @@ import modules.interrogate import modules.memmon import modules.styles import modules.devices as devices -from modules import localization, sd_vae, extensions, script_loading +from modules import localization, sd_vae, extensions, script_loading, errors from modules.paths import models_path, script_path, sd_path @@ -494,7 +494,12 @@ class Options: return False if self.data_labels[key].onchange is not None: - self.data_labels[key].onchange() + try: + self.data_labels[key].onchange() + except Exception as e: + errors.display(e, f"changing setting {key} to {value}") + setattr(self, key, oldval) + return False return True -- cgit v1.2.1 From 8d8a05a3bbb50fdfeab51679a919d2487bd97976 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 12:47:42 +0300 Subject: find configs for models at runtime rather than when starting --- modules/sd_hijack_inpainting.py | 5 ++++- modules/sd_models.py | 31 ++++++++++++++++++------------- 2 files changed, 22 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 3c214a35..31d2c898 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -97,8 +97,11 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F def should_hijack_inpainting(checkpoint_info): + from modules import sd_models + ckpt_basename = os.path.basename(checkpoint_info.filename).lower() - cfg_basename = os.path.basename(checkpoint_info.config).lower() + cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower() + return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename diff --git a/modules/sd_models.py b/modules/sd_models.py index 6846b74a..6dca4ddf 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -20,7 +20,7 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) -CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config']) +CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name']) checkpoints_list = {} checkpoints_loaded = collections.OrderedDict() @@ -48,6 +48,14 @@ def checkpoint_tiles(): return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key) +def find_checkpoint_config(info): + config = os.path.splitext(info.filename)[0] + ".yaml" + if os.path.exists(config): + return config + + return shared.cmd_opts.config + + def list_models(): checkpoints_list.clear() model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"]) @@ -73,7 +81,7 @@ def list_models(): 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.cmd_opts.config) + checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name) shared.opts.data['sd_model_checkpoint'] = 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) @@ -81,12 +89,7 @@ def list_models(): h = model_hash(filename) title, short_model_name = modeltitle(filename, h) - basename, _ = os.path.splitext(filename) - config = basename + ".yaml" - if not os.path.exists(config): - config = shared.cmd_opts.config - - checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config) + checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name) def get_closet_checkpoint_match(searchString): @@ -282,9 +285,10 @@ def enable_midas_autodownload(): def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack checkpoint_info = checkpoint_info or select_checkpoint() + checkpoint_config = find_checkpoint_config(checkpoint_info) - if checkpoint_info.config != shared.cmd_opts.config: - print(f"Loading config from: {checkpoint_info.config}") + if checkpoint_config != shared.cmd_opts.config: + print(f"Loading config from: {checkpoint_config}") if shared.sd_model: sd_hijack.model_hijack.undo_hijack(shared.sd_model) @@ -292,7 +296,7 @@ def load_model(checkpoint_info=None): gc.collect() devices.torch_gc() - sd_config = OmegaConf.load(checkpoint_info.config) + sd_config = OmegaConf.load(checkpoint_config) if should_hijack_inpainting(checkpoint_info): # Hardcoded config for now... @@ -302,7 +306,7 @@ def load_model(checkpoint_info=None): sd_config.model.params.finetune_keys = None # Create a "fake" config with a different name so that we know to unload it when switching models. - checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml")) + checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml")) if not hasattr(sd_config.model.params, "use_ema"): sd_config.model.params.use_ema = False @@ -343,11 +347,12 @@ def reload_model_weights(sd_model=None, info=None): sd_model = shared.sd_model current_checkpoint_info = sd_model.sd_checkpoint_info + checkpoint_config = find_checkpoint_config(current_checkpoint_info) if sd_model.sd_model_checkpoint == checkpoint_info.filename: return - if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): + if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): del sd_model checkpoints_loaded.clear() load_model(checkpoint_info) -- cgit v1.2.1 From 96cf15bedecbed97ef9b70b8413d543a9aee5adf Mon Sep 17 00:00:00 2001 From: MMaker Date: Wed, 4 Jan 2023 05:12:06 -0500 Subject: Add new latent upscale modes --- modules/shared.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 7588c47b..a10f69a9 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -564,8 +564,11 @@ if os.path.exists(config_filename): latent_upscale_default_mode = "Latent" latent_upscale_modes = { - "Latent": "bilinear", - "Latent (nearest)": "nearest", + "Latent": {"mode": "bilinear", "antialias": False}, + "Latent (antialiased)": {"mode": "bilinear", "antialias": True}, + "Latent (bicubic)": {"mode": "bicubic", "antialias": False}, + "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True}, + "Latent (nearest)": {"mode": "nearest", "antialias": False}, } sd_upscalers = [] -- cgit v1.2.1 From 15fd0b8bc4734ea85bca1acfb12b51465ab9817d Mon Sep 17 00:00:00 2001 From: MMaker Date: Wed, 4 Jan 2023 05:12:54 -0500 Subject: Update processing.py --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index a172af0b..7c72b56a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -713,7 +713,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): for i in range(samples.shape[0]): save_intermediate(samples, i) - samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode) + samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. -- cgit v1.2.1 From 4ec6470a1a2d9430b91266426f995e48f59564e1 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 13:26:23 +0300 Subject: fix checkpoint list API --- modules/api/api.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 9c670f00..2b1f180c 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -18,7 +18,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_ from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image -from modules.sd_models import checkpoints_list +from modules.sd_models import checkpoints_list, find_checkpoint_config from modules.realesrgan_model import get_realesrgan_models from modules import devices from typing import List @@ -303,7 +303,7 @@ 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": x.config} for x in checkpoints_list.values()] + 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()] def get_hypernetworks(self): return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] -- cgit v1.2.1 From b2151b934fe0a3613570c6abd7615d3788fd1c8f Mon Sep 17 00:00:00 2001 From: MMaker Date: Wed, 4 Jan 2023 05:36:18 -0500 Subject: Rename bicubic antialiased option Comma was causing the the value in PNG info to be quoted, which causes the upscaler dropdown option to be blank when sending to UI --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index a10f69a9..c1b20081 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -567,7 +567,7 @@ latent_upscale_modes = { "Latent": {"mode": "bilinear", "antialias": False}, "Latent (antialiased)": {"mode": "bilinear", "antialias": True}, "Latent (bicubic)": {"mode": "bicubic", "antialias": False}, - "Latent (bicubic, antialiased)": {"mode": "bicubic", "antialias": True}, + "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True}, "Latent (nearest)": {"mode": "nearest", "antialias": False}, } -- cgit v1.2.1 From 3bd737767b071878ea980e94b8705f603bcf545e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 14:20:32 +0300 Subject: disable broken API logging --- modules/api/api.py | 1 - 1 file changed, 1 deletion(-) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index a6c1d6ed..6267afdc 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -100,7 +100,6 @@ class Api: self.router = APIRouter() self.app = app - init_api_middleware(self.app) self.queue_lock = queue_lock self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) -- cgit v1.2.1 From 0cd6399b8b1699b8b7acad6f0ad2988111fe618e Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 14:29:13 +0300 Subject: fix broken inpainting model --- modules/sd_models.py | 3 --- 1 file changed, 3 deletions(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index 6dca4ddf..a568823d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -305,9 +305,6 @@ def load_model(checkpoint_info=None): sd_config.model.params.unet_config.params.in_channels = 9 sd_config.model.params.finetune_keys = None - # Create a "fake" config with a different name so that we know to unload it when switching models. - checkpoint_info = checkpoint_info._replace(config=checkpoint_config.replace(".yaml", "-inpainting.yaml")) - if not hasattr(sd_config.model.params, "use_ema"): sd_config.model.params.use_ema = False -- cgit v1.2.1 From 11b8160a086c434d5baf4971edda46e6d2126800 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 4 Jan 2023 06:36:57 -0500 Subject: fix typo --- modules/api/api.py | 1 + 1 file changed, 1 insertion(+) (limited to 'modules') diff --git a/modules/api/api.py b/modules/api/api.py index 6267afdc..48a70a44 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -101,6 +101,7 @@ class Api: self.router = APIRouter() self.app = app self.queue_lock = queue_lock + api_middleware(self.app) self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse) self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse) self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse) -- cgit v1.2.1 From 642142556d8ecdea9beb86d7618b628b1803ab98 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 15:09:53 +0300 Subject: use commandline-supplied cuda device name instead of cuda:0 for safetensors PR that doesn't fix anything --- modules/sd_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_models.py b/modules/sd_models.py index ee918f24..76a89e88 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -173,7 +173,7 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None if extension.lower() == ".safetensors": device = map_location or shared.weight_load_location if device is None: - device = "cuda:0" if torch.cuda.is_available() else "cpu" + device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu" pl_sd = safetensors.torch.load_file(checkpoint_file, device=device) else: pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location) -- cgit v1.2.1 From 21ee77db314ede7ccbb18787962347c09a4df0c7 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 4 Jan 2023 08:04:38 -0500 Subject: add cross-attention info --- modules/sd_hijack.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index edcbaf52..fa2cd4bb 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -35,26 +35,35 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th + + optimization_method = None if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward + optimization_method = 'xformers' elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + optimization_method = 'V1' elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): if not invokeAI_mps_available and shared.device.type == 'mps': print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 + optimization_method = 'V1' else: print("Applying cross attention optimization (InvokeAI).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI + optimization_method = 'InvokeAI' elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()): print("Applying cross attention optimization (Doggettx).") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward + optimization_method = 'Doggettx' + + return optimization_method def undo_optimizations(): @@ -75,6 +84,7 @@ class StableDiffusionModelHijack: layers = None circular_enabled = False clip = None + optimization_method = None embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) @@ -94,7 +104,7 @@ class StableDiffusionModelHijack: m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self) m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) - apply_optimizations() + self.optimization_method = apply_optimizations() self.clip = m.cond_stage_model -- cgit v1.2.1 From 1cfd8aec4ae5a6ca1afd67b44cb4ef6dd14d8c34 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 16:05:42 +0300 Subject: make it possible to work with opts.show_progress_every_n_steps = -1 with medvram --- modules/shared.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/shared.py b/modules/shared.py index 4fcc6edd..54a6ba23 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -214,12 +214,13 @@ class State: """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): + if not parallel_processing_allowed: + return + if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0: self.do_set_current_image() def do_set_current_image(self): - if not parallel_processing_allowed: - return if self.current_latent is None: return @@ -231,6 +232,7 @@ class State: self.current_image_sampling_step = self.sampling_step + state = State() artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv')) -- cgit v1.2.1 From 79c682ad4f2d982b26fa1a15044582d1005134f9 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 4 Jan 2023 08:20:42 -0500 Subject: fix jpeg --- modules/extras.py | 2 -- modules/images.py | 2 ++ 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'modules') diff --git a/modules/extras.py b/modules/extras.py index d665440a..7407bfe3 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -19,8 +19,6 @@ from modules.shared import opts import modules.gfpgan_model from modules.ui import plaintext_to_html import modules.codeformer_model -import piexif -import piexif.helper import gradio as gr import safetensors.torch diff --git a/modules/images.py b/modules/images.py index c3a5fc8b..a73be3fa 100644 --- a/modules/images.py +++ b/modules/images.py @@ -22,6 +22,8 @@ from modules.shared import opts, cmd_opts LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +Image.init() # initialize once all known file format handlers + def image_grid(imgs, batch_size=1, rows=None): if rows is None: -- cgit v1.2.1 From 4d66bf2c0d27702cc83b9cc57ebb1f359d18d938 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 17:24:46 +0300 Subject: add infotext to "-before-highres-fix" images --- modules/processing.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index fd7c7015..c03e77e7 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -136,6 +136,7 @@ class StableDiffusionProcessing(): self.all_negative_prompts = None self.all_seeds = None self.all_subseeds = None + self.iteration = 0 def txt2img_image_conditioning(self, x, width=None, height=None): if self.sampler.conditioning_key not in {'hybrid', 'concat'}: @@ -544,6 +545,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: state.job_count = p.n_iter for n in range(p.n_iter): + p.iteration = n + if state.skipped: state.skipped = False @@ -707,7 +710,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not isinstance(image, Image.Image): image = sd_samplers.sample_to_image(image, index, approximation=0) - images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") + info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index) + images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix") if latent_scale_mode is not None: for i in range(samples.shape[0]): -- cgit v1.2.1 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') 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 v1.2.1 From 590c5ae016ae494f4873ca20079b30684ea3060c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 4 Jan 2023 09:48:54 -0500 Subject: update pillow --- modules/images.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index a73be3fa..c3a5fc8b 100644 --- a/modules/images.py +++ b/modules/images.py @@ -22,8 +22,6 @@ from modules.shared import opts, cmd_opts LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) -Image.init() # initialize once all known file format handlers - def image_grid(imgs, batch_size=1, rows=None): if rows is None: -- cgit v1.2.1 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') 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 v1.2.1 From a8eb9e3bf814f72293e474c11e9ff0098859a942 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 18:20:38 +0300 Subject: Revert "Merge pull request #3791 from shirayu/fix/filename" This reverts commit eed58279e7cb0e873ebd88a29609f9bab0f1f3af, reversing changes made to 4ae960b01c6711c66985479f14809dc7fa549fc2. --- modules/images.py | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) (limited to 'modules') diff --git a/modules/images.py b/modules/images.py index 2967fa9a..c3a5fc8b 100644 --- a/modules/images.py +++ b/modules/images.py @@ -447,14 +447,6 @@ def get_next_sequence_number(path, basename): return result + 1 -def truncate_fullpath(full_path, encoding='utf-8'): - dir_name, full_name = os.path.split(full_path) - file_name, file_ext = os.path.splitext(full_name) - max_length = os.statvfs(dir_name).f_namemax - file_name_truncated = file_name.encode(encoding)[:max_length - len(file_ext)].decode(encoding, 'ignore') - return os.path.join(dir_name , file_name_truncated + file_ext) - - def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None): """Save an image. @@ -495,7 +487,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i if save_to_dirs: dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /') - path = truncate_fullpath(os.path.join(path, dirname)) + path = os.path.join(path, dirname) os.makedirs(path, exist_ok=True) @@ -519,13 +511,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i fullfn = None for i in range(500): fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}" - fullfn = truncate_fullpath(os.path.join(path, f"{fn}{file_decoration}.{extension}")) + fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}") if not os.path.exists(fullfn): break else: - fullfn = truncate_fullpath(os.path.join(path, f"{file_decoration}.{extension}")) + fullfn = os.path.join(path, f"{file_decoration}.{extension}") else: - fullfn = truncate_fullpath(os.path.join(path, f"{forced_filename}.{extension}")) + fullfn = os.path.join(path, f"{forced_filename}.{extension}") pnginfo = existing_info or {} if info is not None: -- cgit v1.2.1 From 3dae545a03f5102ba5d9c3f27bb6241824c5a916 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 18:42:51 +0300 Subject: rename weirdly named variables from #3176 --- modules/ui.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index e4859020..184af7ad 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -162,16 +162,14 @@ def save_files(js_data, images, do_make_zip, index): return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}") - - -def calc_time_left(progress, threshold, label, force_display, showTime): +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 showTime) or force_display: + 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: @@ -194,9 +192,9 @@ def check_progress_call(id_part): 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 - showPBText = progress >= 0.01 or shared.state.sampling_step >= 10 + 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, showPBText ) + 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 @@ -204,7 +202,7 @@ def check_progress_call(id_part): progressbar = "" if opts.show_progressbar: - progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if showPBText else ""}
""" + progressbar = f"""
{" " * 2 + str(int(progress*100))+"%" + time_left if show_eta else ""}
""" image = gr_show(False) preview_visibility = gr_show(False) -- cgit v1.2.1 From 097a90b88bb92878cf435c513b4757b5b82ae299 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 19:19:11 +0300 Subject: add XY plot parameters to grid image and do not add them to individual images --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index c7264aff..47712159 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -422,7 +422,7 @@ def fix_seed(p): p.subseed = get_fixed_seed(p.subseed) -def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0): +def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0): index = position_in_batch + iteration * p.batch_size clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers) -- cgit v1.2.1 From 24d4a0841d3cc0e5908b098f65a9caa3fa889af8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 20:10:40 +0300 Subject: train tab visual updates allow setting train tab values from ui-config.json --- modules/ui.py | 35 +++++++++++++++++++++-------------- 1 file changed, 21 insertions(+), 14 deletions(-) (limited to 'modules') diff --git a/modules/ui.py b/modules/ui.py index 72e7b7d2..44f4f3a4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1281,42 +1281,48 @@ def create_ui(): 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 gr.Row(): + with FormRow(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") - with gr.Row(): + train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") - with gr.Row(): + + with FormRow(): embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") - with gr.Row(): + with FormRow(): clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"]) clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False) - batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") - gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") + with FormRow(): + batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") + gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") + 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") 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") steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") - create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") - save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") + + with FormRow(): + create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") + save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") + save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding") preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img") - with gr.Row(): - shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") - tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") - with gr.Row(): - latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") + + shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") + tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") + + latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") with gr.Row(): + train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training") train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork") - train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") params = script_callbacks.UiTrainTabParams(txt2img_preview_params) @@ -1803,6 +1809,7 @@ def create_ui(): visit(img2img_interface, loadsave, "img2img") visit(extras_interface, loadsave, "extras") visit(modelmerger_interface, loadsave, "modelmerger") + visit(train_interface, loadsave, "train") if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)): with open(ui_config_file, "w", encoding="utf8") as file: -- cgit v1.2.1 From 81490780949fffed77493b4bd741e96ec737fe27 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 22:04:40 +0300 Subject: added the option to specify target resolution with possibility of truncating for hires fix; also sampling steps --- modules/generation_parameters_copypaste.py | 9 ++++-- modules/processing.py | 51 +++++++++++++++++++++++++++--- modules/txt2img.py | 5 ++- modules/ui.py | 24 ++++++++++---- 4 files changed, 74 insertions(+), 15 deletions(-) (limited to 'modules') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 4baf4d9a..12a9de3d 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -212,11 +212,10 @@ def restore_old_hires_fix_params(res): firstpass_width = math.ceil(scale * width / 64) * 64 firstpass_height = math.ceil(scale * height / 64) * 64 - hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height - res['Size-1'] = firstpass_width res['Size-2'] = firstpass_height - res['Hires upscale'] = hr_scale + res['Hires resize-1'] = width + res['Hires resize-2'] = height def parse_generation_parameters(x: str): @@ -276,6 +275,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hypernet_hash = res.get("Hypernet hash", None) res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash) + if "Hires resize-1" not in res: + res["Hires resize-1"] = 0 + res["Hires resize-2"] = 0 + restore_old_hires_fix_params(res) return res diff --git a/modules/processing.py b/modules/processing.py index 47712159..9cad05f2 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -662,12 +662,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None - def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, **kwargs): + def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength self.hr_scale = hr_scale self.hr_upscaler = hr_upscaler + self.hr_second_pass_steps = hr_second_pass_steps + self.hr_resize_x = hr_resize_x + self.hr_resize_y = hr_resize_y + self.hr_upscale_to_x = hr_resize_x + self.hr_upscale_to_y = hr_resize_y if firstphase_width != 0 or firstphase_height != 0: print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr) @@ -675,6 +680,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.width = firstphase_width self.height = firstphase_height + self.truncate_x = 0 + self.truncate_y = 0 + def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: if state.job_count == -1: @@ -682,7 +690,38 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 - self.extra_generation_params["Hires upscale"] = self.hr_scale + if self.hr_resize_x == 0 and self.hr_resize_y == 0: + self.extra_generation_params["Hires upscale"] = self.hr_scale + self.hr_upscale_to_x = int(self.width * self.hr_scale) + self.hr_upscale_to_y = int(self.height * self.hr_scale) + else: + self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}" + + if self.hr_resize_y == 0: + self.hr_upscale_to_x = self.hr_resize_x + self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width + elif self.hr_resize_x == 0: + self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height + self.hr_upscale_to_y = self.hr_resize_y + else: + target_w = self.hr_resize_x + target_h = self.hr_resize_y + src_ratio = self.width / self.height + dst_ratio = self.hr_resize_x / self.hr_resize_y + + if src_ratio < dst_ratio: + self.hr_upscale_to_x = self.hr_resize_x + self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width + else: + self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height + self.hr_upscale_to_y = self.hr_resize_y + + self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f + self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f + + if self.hr_second_pass_steps: + self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps + if self.hr_upscaler is not None: self.extra_generation_params["Hires upscaler"] = self.hr_upscaler @@ -699,8 +738,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr: return samples - target_width = int(self.width * self.hr_scale) - target_height = int(self.height * self.hr_scale) + target_width = self.hr_upscale_to_x + target_height = self.hr_upscale_to_y def save_intermediate(image, index): """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images""" @@ -755,13 +794,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) + samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2] + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) return samples diff --git a/modules/txt2img.py b/modules/txt2img.py index e189a899..38b5f591 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, *args): +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): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -35,6 +35,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: denoising_strength=denoising_strength if enable_hr else None, hr_scale=hr_scale, hr_upscaler=hr_upscaler, + hr_second_pass_steps=hr_second_pass_steps, + hr_resize_x=hr_resize_x, + hr_resize_y=hr_resize_y, ) p.scripts = modules.scripts.scripts_txt2img diff --git a/modules/ui.py b/modules/ui.py index 44f4f3a4..04091e67 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -637,10 +637,10 @@ def create_sampler_and_steps_selection(choices, tabname): with FormRow(elem_id=f"sampler_selection_{tabname}"): sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") sampler_index.save_to_config = True - steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20) + steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) else: with FormGroup(elem_id=f"sampler_selection_{tabname}"): - steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20) + steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20) sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index") return steps, sampler_index @@ -709,10 +709,16 @@ def create_ui(): enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr") elif category == "hires_fix": - with FormRow(visible=False, elem_id="txt2img_hires_fix") as hr_options: - hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) - hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") + with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options: + with FormRow(elem_id="txt2img_hires_fix_row1"): + hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) + hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps") + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength") + + with FormRow(elem_id="txt2img_hires_fix_row2"): + hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale") + hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x") + hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y") elif category == "batch": if not opts.dimensions_and_batch_together: @@ -753,6 +759,9 @@ def create_ui(): denoising_strength, hr_scale, hr_upscaler, + hr_second_pass_steps, + hr_resize_x, + hr_resize_y, ] + custom_inputs, outputs=[ @@ -804,6 +813,9 @@ def create_ui(): (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), (hr_scale, "Hires upscale"), (hr_upscaler, "Hires upscaler"), + (hr_second_pass_steps, "Hires steps"), + (hr_resize_x, "Hires resize-1"), + (hr_resize_y, "Hires resize-2"), *modules.scripts.scripts_txt2img.infotext_fields ] parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields) -- cgit v1.2.1 From bc43293c640aef65df3136de9e5bd8b7e79eb3e0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 4 Jan 2023 23:56:43 +0300 Subject: fix incorrect display/calculation for number of steps for hires fix in progress bars --- modules/processing.py | 9 ++++++--- modules/sd_samplers.py | 5 +++-- modules/shared.py | 4 +++- 3 files changed, 12 insertions(+), 6 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index 9cad05f2..f28e7212 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -685,10 +685,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: - if state.job_count == -1: - state.job_count = self.n_iter * 2 - else: + if not state.processing_has_refined_job_count: + if state.job_count == -1: + state.job_count = self.n_iter + + shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count) state.job_count = state.job_count * 2 + state.processing_has_refined_job_count = True if self.hr_resize_x == 0 and self.hr_resize_y == 0: self.extra_generation_params["Hires upscale"] = self.hr_scale diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index e904d860..3851a77f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -97,8 +97,9 @@ sampler_extra_params = { def setup_img2img_steps(p, steps=None): if opts.img2img_fix_steps or steps is not None: - steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 - t_enc = p.steps - 1 + requested_steps = (steps or p.steps) + steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 + t_enc = requested_steps - 1 else: steps = p.steps t_enc = int(min(p.denoising_strength, 0.999) * steps) diff --git a/modules/shared.py b/modules/shared.py index 54a6ba23..04c545ee 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -153,6 +153,7 @@ class State: job = "" job_no = 0 job_count = 0 + processing_has_refined_job_count = False job_timestamp = '0' sampling_step = 0 sampling_steps = 0 @@ -194,6 +195,7 @@ class State: def begin(self): self.sampling_step = 0 self.job_count = -1 + self.processing_has_refined_job_count = False self.job_no = 0 self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") self.current_latent = None @@ -608,7 +610,7 @@ class TotalTQDM: return if self._tqdm is None: self.reset() - self._tqdm.total=new_total + self._tqdm.total = new_total def clear(self): if self._tqdm is not None: -- cgit v1.2.1 From 99b67cff0b48c4a1ad6e14d9cc591b11db6e293c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 5 Jan 2023 01:25:52 +0300 Subject: make hires fix not do anything if the user chooses the second pass resolution to be the same as first pass resolution --- modules/processing.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) (limited to 'modules') diff --git a/modules/processing.py b/modules/processing.py index f28e7212..7e853287 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -683,16 +683,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = 0 self.truncate_y = 0 + def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: - if not state.processing_has_refined_job_count: - if state.job_count == -1: - state.job_count = self.n_iter - - shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count) - state.job_count = state.job_count * 2 - state.processing_has_refined_job_count = True - if self.hr_resize_x == 0 and self.hr_resize_y == 0: self.extra_generation_params["Hires upscale"] = self.hr_scale self.hr_upscale_to_x = int(self.width * self.hr_scale) @@ -722,6 +715,22 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f + # special case: the user has chosen to do nothing + if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height: + self.enable_hr = False + self.denoising_strength = None + self.extra_generation_params.pop("Hires upscale", None) + self.extra_generation_params.pop("Hires resize", None) + return + + if not state.processing_has_refined_job_count: + if state.job_count == -1: + state.job_count = self.n_iter + + shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count) + state.job_count = state.job_count * 2 + state.processing_has_refined_job_count = True + if self.hr_second_pass_steps: self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps -- cgit v1.2.1