From e32b4d4c04a31b22051740e5f26e16960464f787 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Fri, 3 Mar 2023 18:53:15 +0100
Subject: Implemented different noise offset

---
 environment.yaml       |  2 +-
 train_dreambooth.py    |  4 ++--
 train_lora.py          |  2 +-
 train_ti.py            |  4 ++--
 training/functional.py | 31 ++++++++++---------------------
 training/util.py       |  1 -
 6 files changed, 16 insertions(+), 28 deletions(-)

diff --git a/environment.yaml b/environment.yaml
index 4899709..018a9ab 100644
--- a/environment.yaml
+++ b/environment.yaml
@@ -24,4 +24,4 @@ dependencies:
       - setuptools==65.6.3
       - test-tube>=0.7.5
       - transformers==4.26.1
-      - triton==2.0.0a2
+      - triton==2.0.0
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 6d699f3..8571dff 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -438,7 +438,7 @@ def main():
 
     accelerator = Accelerator(
         log_with=LoggerType.TENSORBOARD,
-        logging_dir=f"{output_dir}",
+        project_dir=f"{output_dir}",
         mixed_precision=args.mixed_precision
     )
 
@@ -526,7 +526,7 @@ def main():
         with_prior_preservation=args.num_class_images != 0,
         prior_loss_weight=args.prior_loss_weight,
         no_val=args.valid_set_size == 0,
-        # low_freq_noise=0,
+        # noise_offset=0,
     )
 
     checkpoint_output_dir = output_dir / "model"
diff --git a/train_lora.py b/train_lora.py
index 0a3d4c9..e213e3d 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -398,7 +398,7 @@ def main():
 
     accelerator = Accelerator(
         log_with=LoggerType.TENSORBOARD,
-        logging_dir=f"{output_dir}",
+        project_dir=f"{output_dir}",
         mixed_precision=args.mixed_precision
     )
 
diff --git a/train_ti.py b/train_ti.py
index 394711f..bc9348d 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -517,7 +517,7 @@ def main():
 
     accelerator = Accelerator(
         log_with=LoggerType.TENSORBOARD,
-        logging_dir=f"{output_dir}",
+        project_dir=f"{output_dir}",
         mixed_precision=args.mixed_precision
     )
 
@@ -607,7 +607,7 @@ def main():
         with_prior_preservation=args.num_class_images != 0,
         prior_loss_weight=args.prior_loss_weight,
         no_val=args.valid_set_size == 0,
-        # low_freq_noise=0,
+        noise_offset=0,
         strategy=textual_inversion_strategy,
         num_train_epochs=args.num_train_epochs,
         gradient_accumulation_steps=args.gradient_accumulation_steps,
diff --git a/training/functional.py b/training/functional.py
index 2d582bf..36269f0 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -253,7 +253,7 @@ def loss_step(
     text_encoder: CLIPTextModel,
     with_prior_preservation: bool,
     prior_loss_weight: float,
-    low_freq_noise: float,
+    noise_offset: float,
     seed: int,
     step: int,
     batch: dict[str, Any],
@@ -268,30 +268,19 @@ def loss_step(
     generator = torch.Generator(device=latents.device).manual_seed(seed + step) if eval else None
 
     # Sample noise that we'll add to the latents
-    noise = torch.randn(
-        latents.shape,
+    offsets = noise_offset * torch.randn(
+        latents.shape[0], 1, 1, 1,
         dtype=latents.dtype,
         layout=latents.layout,
         device=latents.device,
         generator=generator
+    ).expand(latents.shape)
+    noise = torch.normal(
+        mean=offsets,
+        std=1,
+        generator=generator,
     )
 
-    if low_freq_noise != 0:
-        low_freq_factor = low_freq_noise * torch.randn(
-            latents.shape[0], 1, 1, 1,
-            dtype=latents.dtype,
-            layout=latents.layout,
-            device=latents.device,
-            generator=generator
-        )
-        noise = noise * (1 - low_freq_factor) + low_freq_factor * torch.randn(
-            latents.shape[0], latents.shape[1], 1, 1,
-            dtype=latents.dtype,
-            layout=latents.layout,
-            device=latents.device,
-            generator=generator
-        )
-
     # Sample a random timestep for each image
     timesteps = torch.randint(
         0,
@@ -576,7 +565,7 @@ def train(
     global_step_offset: int = 0,
     with_prior_preservation: bool = False,
     prior_loss_weight: float = 1.0,
-    low_freq_noise: float = 0.1,
+    noise_offset: float = 0.2,
     **kwargs,
 ):
     text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, extra = strategy.prepare(
@@ -611,7 +600,7 @@ def train(
         text_encoder,
         with_prior_preservation,
         prior_loss_weight,
-        low_freq_noise,
+        noise_offset,
         seed,
     )
 
diff --git a/training/util.py b/training/util.py
index c8524de..8bd8a83 100644
--- a/training/util.py
+++ b/training/util.py
@@ -1,6 +1,5 @@
 from pathlib import Path
 import json
-import copy
 from typing import Iterable, Any
 from contextlib import contextmanager
 
-- 
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