Unverified Commit 17a2076f authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
Browse files

Merge pull request #3928 from R-N/validate-before-load 

Optimize training a little
parents 3dc9a43f 3d58510f

modules/hypernetworks/hypernetwork.py

+43 −26
Original line number Diff line number Diff line
@@ -335,7 +335,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 
    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

    from modules import images



    assert hypernetwork_name, 'hypernetwork not selected'

    save_hypernetwork_every = save_hypernetwork_every or 0

    create_image_every = create_image_every or 0

    textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, data_root, template_file, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")



    path = shared.hypernetworks.get(hypernetwork_name, None)

    shared.loaded_hypernetwork = Hypernetwork()
@@ -361,18 +363,25 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 
    else:

        images_dir = None



    hypernetwork = shared.loaded_hypernetwork

    checkpoint = sd_models.select_checkpoint()



    ititial_step = hypernetwork.step or 0

    if ititial_step >= steps:

        shared.state.textinfo = f"Model has already been trained beyond specified max steps"

        return hypernetwork, filename



    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    

    # 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"):

        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, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)



    if unload:

        shared.sd_model.cond_stage_model.to(devices.cpu)

        shared.sd_model.first_stage_model.to(devices.cpu)



    hypernetwork = shared.loaded_hypernetwork

    weights = hypernetwork.weights()

    for weight in weights:

        weight.requires_grad = True



    size = len(ds.indexes)

    loss_dict = defaultdict(lambda : deque(maxlen = 1024))

    losses = torch.zeros((size,))
@@ -380,20 +389,18 @@ 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 = "<none>"

    last_saved_image = "<none>"

    forced_filename = "<none>"



    ititial_step = hypernetwork.step or 0

    if ititial_step > steps:

        return hypernetwork, filename



    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    weights = hypernetwork.weights()

    for weight in weights:

        weight.requires_grad = True

    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...

    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)



    steps_without_grad = 0



    last_saved_file = "<none>"

    last_saved_image = "<none>"

    forced_filename = "<none>"



    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)

    for i, entries in pbar:

        hypernetwork.step = i + ititial_step
@@ -446,9 +453,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log 


        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:

            # Before saving, change name to match current checkpoint.

            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'

            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')

            hypernetwork.save(last_saved_file)

            hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

            last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')

            save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)



        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

            "loss": f"{previous_mean_loss:.7f}",
@@ -509,13 +516,23 @@ Last saved image: {html.escape(last_saved_image)}<br/> 
"""

        

    report_statistics(loss_dict)

    checkpoint = sd_models.select_checkpoint()



    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

    save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)



    return hypernetwork, filename



def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):

    old_hypernetwork_name = hypernetwork.name

    old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None

    old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None

    try:

        hypernetwork.sd_checkpoint = checkpoint.hash

        hypernetwork.sd_checkpoint_name = checkpoint.model_name

    # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).

        hypernetwork.name = hypernetwork_name

    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')

        hypernetwork.save(filename)



    return hypernetwork, filename
 
    except:

        hypernetwork.sd_checkpoint = old_sd_checkpoint

        hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name

        hypernetwork.name = old_hypernetwork_name

        raise

modules/textual_inversion/dataset.py

+2 −0
Original line number Diff line number Diff line
@@ -42,6 +42,8 @@ class PersonalizedBase(Dataset): 
        self.lines = lines



        assert data_root, 'dataset directory not specified'

        assert os.path.isdir(data_root), "Dataset directory doesn't exist"

        assert os.listdir(data_root), "Dataset directory is empty"



        cond_model = shared.sd_model.cond_stage_model

modules/textual_inversion/textual_inversion.py

+62 −25
Original line number Diff line number Diff line
@@ -119,7 +119,7 @@ class EmbeddingDatabase: 
            vec = emb.detach().to(devices.device, dtype=torch.float32)

            embedding = Embedding(vec, name)

            embedding.step = data.get('step', None)

            embedding.sd_checkpoint = data.get('hash', None)

            embedding.sd_checkpoint = data.get('sd_checkpoint', None)

            embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)

            self.register_embedding(embedding, shared.sd_model)
@@ -204,9 +204,30 @@ def write_loss(log_directory, filename, step, epoch_len, values): 
            **values,

        })



def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):

    assert model_name, f"{name} not selected"

    assert learn_rate, "Learning rate is empty or 0"

    assert isinstance(batch_size, int), "Batch size must be integer"

    assert batch_size > 0, "Batch size must be positive"

    assert data_root, "Dataset directory is empty"

    assert os.path.isdir(data_root), "Dataset directory doesn't exist"

    assert os.listdir(data_root), "Dataset directory is empty"

    assert template_file, "Prompt template file is empty"

    assert os.path.isfile(template_file), "Prompt template file doesn't exist"

    assert steps, "Max steps is empty or 0"

    assert isinstance(steps, int), "Max steps must be integer"

    assert steps > 0 , "Max steps must be positive"

    assert isinstance(save_model_every, int), "Save {name} must be integer"

    assert save_model_every >= 0 , "Save {name} must be positive or 0"

    assert isinstance(create_image_every, int), "Create image must be integer"

    assert create_image_every >= 0 , "Create image must be positive or 0"

    if save_model_every or create_image_every:

        assert log_directory, "Log directory is empty"



def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    assert embedding_name, 'embedding not selected'

    save_embedding_every = save_embedding_every or 0

    create_image_every = create_image_every or 0

    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")



    shared.state.textinfo = "Initializing textual inversion training..."

    shared.state.job_count = steps
@@ -235,14 +256,25 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc 


    cond_model = shared.sd_model.cond_stage_model



    hijack = sd_hijack.model_hijack



    embedding = hijack.embedding_db.word_embeddings[embedding_name]

    checkpoint = sd_models.select_checkpoint()



    ititial_step = embedding.step or 0

    if ititial_step >= steps:

        shared.state.textinfo = f"Model has already been trained beyond specified max steps"

        return embedding, filename



    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)



    # 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"):

        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, device=devices.device, template_file=template_file, batch_size=batch_size)



    hijack = sd_hijack.model_hijack



    embedding = hijack.embedding_db.word_embeddings[embedding_name]

    embedding.vec.requires_grad = True

    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)



    losses = torch.zeros((32,))
@@ -251,13 +283,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc 
    forced_filename = "<none>"

    embedding_yet_to_be_embedded = False



    ititial_step = embedding.step or 0

    if ititial_step > steps:

        return embedding, filename



    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)



    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

    for i, entries in pbar:

        embedding.step = i + ititial_step
@@ -290,9 +315,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc 


        if embedding_dir is not None and steps_done % save_embedding_every == 0:

            # Before saving, change name to match current checkpoint.

            embedding.name = f'{embedding_name}-{steps_done}'

            last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')

            embedding.save(last_saved_file)

            embedding_name_every = f'{embedding_name}-{steps_done}'

            last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')

            save_embedding(embedding, 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, len(ds), {
@@ -373,14 +398,26 @@ Last saved image: {html.escape(last_saved_image)}<br/> 
</p>

"""



    checkpoint = sd_models.select_checkpoint()

    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

    save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)



    return embedding, filename



def save_embedding(embedding, 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

    old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None

    try:

        embedding.sd_checkpoint = checkpoint.hash

        embedding.sd_checkpoint_name = checkpoint.model_name

        if remove_cached_checksum:

            embedding.cached_checksum = None

    # Before saving for the last time, change name back to base name (as opposed to the save_embedding_every step-suffixed naming convention).

        embedding.name = embedding_name

    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding.name}.pt')

        embedding.save(filename)



    return embedding, filename
 
    except:

        embedding.sd_checkpoint = old_sd_checkpoint

        embedding.sd_checkpoint_name = old_sd_checkpoint_name

        embedding.name = old_embedding_name

        embedding.cached_checksum = old_cached_checksum

        raise

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