From edb10092de516dda5271130ed53628387780a859 Mon Sep 17 00:00:00 2001 From: Shondoit Date: Thu, 12 Jan 2023 16:29:00 +0100 Subject: [PATCH] Add ability to choose using weighted loss or not --- modules/hypernetworks/hypernetwork.py | 13 +++++++++---- modules/textual_inversion/dataset.py | 15 ++++++++++----- modules/textual_inversion/textual_inversion.py | 13 +++++++++---- modules/ui.py | 4 ++++ 4 files changed, 32 insertions(+), 13 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 9c79b7d0..f4fb69e0 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, shared.reload_hypernetworks() -def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images @@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi pin_memory = shared.opts.pin_memory - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight) if shared.opts.save_training_settings_to_txt: saved_params = dict( @@ -640,14 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) - w = batch.weight.to(devices.device, non_blocking=pin_memory) + if use_weight: + w = batch.weight.to(devices.device, non_blocking=pin_memory) if tag_drop_out != 0 or shuffle_tags: shared.sd_model.cond_stage_model.to(devices.device) c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory) shared.sd_model.cond_stage_model.to(devices.cpu) else: c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) - loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step + if use_weight: + loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step + del w + else: + loss = shared.sd_model.forward(x, c)[0] / gradient_step del x del c diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index f4ce4552..1568b2b8 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -31,7 +31,7 @@ class DatasetEntry: class PersonalizedBase(Dataset): - def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False): + def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False): re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None self.placeholder_token = placeholder_token @@ -64,7 +64,7 @@ class PersonalizedBase(Dataset): image = Image.open(path) #Currently does not work for single color transparency #We would need to read image.info['transparency'] for that - if 'A' in image.getbands(): + if use_weight and 'A' in image.getbands(): alpha_channel = image.getchannel('A') image = image.convert('RGB') if not varsize: @@ -104,7 +104,7 @@ class PersonalizedBase(Dataset): latent_sampling_method = "once" latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) - if alpha_channel is not None: + if use_weight and alpha_channel is not None: channels, *latent_size = latent_sample.shape weight_img = alpha_channel.resize(latent_size) npweight = np.array(weight_img).astype(np.float32) @@ -113,9 +113,11 @@ class PersonalizedBase(Dataset): #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default. weight -= weight.min() weight /= weight.mean() - else: + elif use_weight: #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later weight = torch.ones([channels] + latent_size) + else: + weight = None if latent_sampling_method == "random": entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight) @@ -219,7 +221,10 @@ class BatchLoader: self.cond_text = [entry.cond_text for entry in data] self.cond = [entry.cond for entry in data] self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1) - self.weight = torch.stack([entry.weight for entry in data]).squeeze(1) + if all(entry.weight is not None for entry in data): + self.weight = torch.stack([entry.weight for entry in data]).squeeze(1) + else: + self.weight = None #self.emb_index = [entry.emb_index for entry in data] #print(self.latent_sample.device) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 8853c868..c63c7d1d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -351,7 +351,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat assert log_directory, "Log directory is empty" -def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): +def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): save_embedding_every = save_embedding_every or 0 create_image_every = create_image_every or 0 template_file = textual_inversion_templates.get(template_filename, None) @@ -410,7 +410,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st pin_memory = shared.opts.pin_memory - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight) if shared.opts.save_training_settings_to_txt: save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()}) @@ -480,7 +480,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st with devices.autocast(): x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) - w = batch.weight.to(devices.device, non_blocking=pin_memory) + if use_weight: + w = batch.weight.to(devices.device, non_blocking=pin_memory) c = shared.sd_model.cond_stage_model(batch.cond_text) if is_training_inpainting_model: @@ -491,7 +492,11 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st else: cond = c - loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step + if use_weight: + loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step + del w + else: + loss = shared.sd_model.forward(x, cond)[0] / gradient_step del x _loss_step += loss.item() diff --git a/modules/ui.py b/modules/ui.py index f5df1ffe..efb87c23 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1191,6 +1191,8 @@ def create_ui(): 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") + use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight") + 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") @@ -1304,6 +1306,7 @@ def create_ui(): shuffle_tags, tag_drop_out, latent_sampling_method, + use_weight, create_image_every, save_embedding_every, template_file, @@ -1337,6 +1340,7 @@ def create_ui(): shuffle_tags, tag_drop_out, latent_sampling_method, + use_weight, create_image_every, save_embedding_every, template_file,