Merge pull request #6481 from guaneec/varsize

Allow mixed image sizes in TI/HN training

2 files changed, 16 insertions, 13 deletions

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 88d68c76..bcad6848 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

@@ -25,16 +25,15 @@ class DatasetEntry:
         self.cond = cond

         self.cond_text = cond_text

         self.pixel_values = pixel_values

+        self.img_shape = img_shape

 

 

 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'):

+    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):

         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

 

-        self.width = width

-        self.height = height

         self.flip = transforms.RandomHorizontalFlip(p=flip_p)

 

         self.dataset = []

@@ -47,6 +46,8 @@ class PersonalizedBase(Dataset):
         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"

+        if varsize:

+            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)]

 

@@ -59,7 +60,9 @@ 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 not varsize:

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

             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 217fe9eb..ad76297e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -296,8 +296,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
     if save_model_every or create_image_every:

         assert log_directory, "Log directory is empty"

 

-

-def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, 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):

+def train_embedding(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_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

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

@@ -351,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 

     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)

+    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)

 

     if shared.opts.save_training_settings_to_txt:

         save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.hash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})

@@ -493,8 +492,8 @@ 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