From a176d89487d92f5a5b152401e5c424b34ff43b96 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 13 Jan 2023 14:32:15 +0300
Subject: print bucket sizes for training without resizing images #6620 fix an
 error when generating a picture with embedding in it

---
 modules/textual_inversion/dataset.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

(limited to 'modules/textual_inversion/dataset.py')

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index b47414f3..d31963d4 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -118,6 +118,12 @@ class PersonalizedBase(Dataset):
         self.gradient_step = min(gradient_step, self.length // self.batch_size)
         self.latent_sampling_method = latent_sampling_method
 
+        if len(groups) > 1:
+            print("Buckets:")
+            for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
+                print(f"  {w}x{h}: {len(ids)}")
+            print()
+
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         tags = filename_text.split(',')
@@ -140,8 +146,11 @@ class PersonalizedBase(Dataset):
             entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
         return entry
 
+
 class GroupedBatchSampler(Sampler):
     def __init__(self, data_source: PersonalizedBase, batch_size: int):
+        super().__init__(data_source)
+
         n = len(data_source)
         self.groups = data_source.groups
         self.len = n_batch = n // batch_size
@@ -150,21 +159,28 @@ class GroupedBatchSampler(Sampler):
         self.n_rand_batches = nrb = n_batch - sum(self.base)
         self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
         self.batch_size = batch_size
+
     def __len__(self):
         return self.len
+
     def __iter__(self):
         b = self.batch_size
+
         for g in self.groups:
             shuffle(g)
+
         batches = []
         for g in self.groups:
             batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
         for _ in range(self.n_rand_batches):
             rand_group = choices(self.groups, self.probs)[0]
             batches.append(choices(rand_group, k=b))
+
         shuffle(batches)
+
         yield from batches
 
+
 class PersonalizedDataLoader(DataLoader):
     def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
         super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
-- 
cgit v1.2.3