diff options
| -rw-r--r-- | modules/textual_inversion/dataset.py | 18 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 4 |
2 files changed, 13 insertions, 9 deletions
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 88d68c76..375178ed 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -17,7 +17,7 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*") 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, img_shape=None):
self.filename = filename
self.filename_text = filename_text
self.latent_dist = latent_dist
@@ -25,6 +25,7 @@ class DatasetEntry: self.cond = cond
self.cond_text = cond_text
self.pixel_values = pixel_values
+ self.img_shape = img_shape
class PersonalizedBase(Dataset):
@@ -33,8 +34,6 @@ class PersonalizedBase(Dataset): self.placeholder_token = placeholder_token
- self.width = width
- self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.dataset = []
@@ -59,7 +58,11 @@ class PersonalizedBase(Dataset): if shared.state.interrupted:
raise Exception("interrupted")
try:
- image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
+ image = Image.open(path).convert('RGB')
+ if width < 2000:
+ image = image.resize((width, height), PIL.Image.BICUBIC)
+ else:
+ assert batch_size == 1, 'variable img size must have batch size 1'
except Exception:
continue
@@ -88,14 +91,14 @@ class PersonalizedBase(Dataset): 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)
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
elif latent_sampling_method == "deterministic":
# Works only for DiagonalGaussianDistribution
latent_dist.std = 0
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)
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, img_shape=image.size)
elif latent_sampling_method == "random":
- entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, img_shape=image.size)
if not (self.tag_drop_out != 0 or self.shuffle_tags):
entry.cond_text = self.create_text(filename_text)
@@ -151,6 +154,7 @@ 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.img_shape = [entry.img_shape for entry in data]
#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 45882ed6..9f96d0fd 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -451,8 +451,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ else:
p.prompt = batch.cond_text[0]
p.steps = 20
- p.width = training_width
- p.height = training_height
+ p.width = batch.img_shape[0][0]
+ p.height = batch.img_shape[0][1]
preview_text = p.prompt
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