Merge pull request #6700 from Shondoit/weighted-learning

    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

    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(

                with devices.autocast():

                    x = batch.latent_sample.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(x, c)[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

import torch

from torch.nn.functional import silu

from types import MethodType

import modules.textual_inversion.textual_inversion

from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint

    pass

def weighted_loss(sd_model, pred, target, mean=True):

    #Calculate the weight normally, but ignore the mean

    loss = sd_model._old_get_loss(pred, target, mean=False)

    #Check if we have weights available

    weight = getattr(sd_model, '_custom_loss_weight', None)

    if weight is not None:

        loss *= weight

    #Return the loss, as mean if specified

    return loss.mean() if mean else loss

def weighted_forward(sd_model, x, c, w, *args, **kwargs):

    try:

        #Temporarily append weights to a place accessible during loss calc

        sd_model._custom_loss_weight = w

        #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely

        #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set

        if not hasattr(sd_model, '_old_get_loss'):

            sd_model._old_get_loss = sd_model.get_loss

        sd_model.get_loss = MethodType(weighted_loss, sd_model)

        #Run the standard forward function, but with the patched 'get_loss'

        return sd_model.forward(x, c, *args, **kwargs)

    finally:

        try:

            #Delete temporary weights if appended

            del sd_model._custom_loss_weight

        except AttributeError as e:

            pass

        #If we have an old loss function, reset the loss function to the original one

        if hasattr(sd_model, '_old_get_loss'):

            sd_model.get_loss = sd_model._old_get_loss

            del sd_model._old_get_loss

def apply_weighted_forward(sd_model):

    #Add new function 'weighted_forward' that can be called to calc weighted loss

    sd_model.weighted_forward = MethodType(weighted_forward, sd_model)

def undo_weighted_forward(sd_model):

    try:

        del sd_model.weighted_forward

    except AttributeError as e:

        pass

class StableDiffusionModelHijack:

    fixes = None

    comments = []

            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_weighted_forward(m)

        if m.cond_stage_key == "edit":

            sd_hijack_unet.hijack_ddpm_edit()

            m.cond_stage_model = m.cond_stage_model.wrapped

        undo_optimizations()

        undo_weighted_forward(m)

        self.apply_circular(False)

        self.layers = None

class DatasetEntry:

    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):

    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):

        self.filename = filename

        self.filename_text = filename_text

        self.weight = weight

        self.latent_dist = latent_dist

        self.latent_sample = latent_sample

        self.cond = cond

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

        print("Preparing dataset...")

        for path in tqdm.tqdm(self.image_paths):

            alpha_channel = None

            if shared.state.interrupted:

                raise Exception("interrupted")

            try:

                image = Image.open(path).convert('RGB')

                image = Image.open(path)

                #Currently does not work for single color transparency

                #We would need to read image.info['transparency'] for that

                if use_weight and 'A' in image.getbands():

                    alpha_channel = image.getchannel('A')

                image = image.convert('RGB')

                if not varsize:

                    image = image.resize((width, height), PIL.Image.BICUBIC)

            except Exception:

            with devices.autocast():

                latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))

            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):

                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)

                latent_sampling_method = "once"

                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)

            elif latent_sampling_method == "deterministic":

            #Perform latent sampling, even for random sampling.

            #We need the sample dimensions for the weights

            if latent_sampling_method == "deterministic":

                if isinstance(latent_dist, DiagonalGaussianDistribution):

                    # Works only for DiagonalGaussianDistribution

                    latent_dist.std = 0

                else:

                    latent_sampling_method = "once"

            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)

                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)

            elif latent_sampling_method == "random":

                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)

            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)

                #Repeat for every channel in the latent sample

                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)

                #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()

            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)

            else:

                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)

            if not (self.tag_drop_out != 0 or self.shuffle_tags):

                entry.cond_text = self.create_text(filename_text)

            del torchdata

            del latent_dist

            del latent_sample

            del weight

        self.length = len(self.dataset)

        self.groups = list(groups.values())

        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)

        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)

        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)

    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()})

                with devices.autocast():

                    x = batch.latent_sample.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:

                    else:

                        cond = c

                    loss = shared.sd_model(x, cond)[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()

                        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")

                shuffle_tags,

                tag_drop_out,

                latent_sampling_method,

                use_weight,

                create_image_every,

                save_embedding_every,

                template_file,

                shuffle_tags,

                tag_drop_out,

                latent_sampling_method,

                use_weight,

                create_image_every,

                save_embedding_every,

                template_file,