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fixed textual inversion training with inpainting models

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This commit is contained in:
Nerogar 2022-10-23 14:05:25 +02:00
parent 198a1ffcfc
commit cffc240a73
1 changed files with 26 additions and 1 deletions
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27
modules/textual_inversion/textual_inversion.py
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@ -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 = "<none>"
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()