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remove/simplify some changes from #6481

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AUTOMATIC 2023-01-09 22:52:23 +03:00
parent bdd57ad073
commit 43bb5190fc
3 changed files with 8 additions and 12 deletions
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14
modules/textual_inversion/dataset.py
View file

@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")
class DatasetEntry: class DatasetEntry:
def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, img_shape=None): def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
self.filename = filename self.filename = filename
self.filename_text = filename_text self.filename_text = filename_text
self.latent_dist = latent_dist self.latent_dist = latent_dist
@ -25,7 +25,6 @@ class DatasetEntry:
self.cond = cond self.cond = cond
self.cond_text = cond_text self.cond_text = cond_text
self.pixel_values = pixel_values self.pixel_values = pixel_values
self.img_shape = img_shape
class PersonalizedBase(Dataset): class PersonalizedBase(Dataset):
@ -46,12 +45,10 @@ class PersonalizedBase(Dataset):
assert data_root, 'dataset directory not specified' assert data_root, 'dataset directory not specified'
assert os.path.isdir(data_root), "Dataset directory doesn't exist" assert os.path.isdir(data_root), "Dataset directory doesn't exist"
assert os.listdir(data_root), "Dataset directory is empty" assert os.listdir(data_root), "Dataset directory is empty"
if varsize: assert batch_size == 1 or not varsize, 'variable img size must have batch size 1'
assert batch_size == 1, 'variable img size must have batch size 1'
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
self.shuffle_tags = shuffle_tags self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out self.tag_drop_out = tag_drop_out
@ -91,14 +88,14 @@ class PersonalizedBase(Dataset):
if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)): 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_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
latent_sampling_method = "once" latent_sampling_method = "once"
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size) entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "deterministic": elif latent_sampling_method == "deterministic":
# Works only for DiagonalGaussianDistribution # Works only for DiagonalGaussianDistribution
latent_dist.std = 0 latent_dist.std = 0
latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu) 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, img_shape=image.size) entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
elif latent_sampling_method == "random": elif latent_sampling_method == "random":
entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size) entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
if not (self.tag_drop_out != 0 or self.shuffle_tags): if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text) entry.cond_text = self.create_text(filename_text)
@ -154,7 +151,6 @@ class BatchLoader:
self.cond_text = [entry.cond_text for entry in data] self.cond_text = [entry.cond_text for entry in data]
self.cond = [entry.cond 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.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
self.img_shape = [entry.img_shape for entry in data]
#self.emb_index = [entry.emb_index for entry in data] #self.emb_index = [entry.emb_index for entry in data]
#print(self.latent_sample.device) #print(self.latent_sample.device)

4
modules/textual_inversion/textual_inversion.py
View file

@ -492,8 +492,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
else: else:
p.prompt = batch.cond_text[0] p.prompt = batch.cond_text[0]
p.steps = 20 p.steps = 20
p.width = batch.img_shape[0][0] p.width = training_width
p.height = batch.img_shape[0][1] p.height = training_height
preview_text = p.prompt preview_text = p.prompt

2
modules/ui.py
View file

@ -1348,7 +1348,7 @@ def create_ui():
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") 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_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") training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
varsize = gr.Checkbox(label="Ignore dimension settings and do not resize images", value=False, elem_id="train_varsize") varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
with FormRow(): with FormRow():