fix typos, add comments etc.

library/sdxl_model_util.py

@@ -259,7 +259,7 @@ def load_models_from_sdxl_checkpoint(model_version, ckpt_path, map_location, dty
         elif k.startswith("conditioner.embedders.1.model."):
             te2_sd[k] = state_dict.pop(k)
 
-    info1 = _load_state_dict_on_device(text_model1, te1_sd, device=map_location)    # remain fp32
+    info1 = _load_state_dict_on_device(text_model1, te1_sd, device=map_location)  # remain fp32
     print("text encoder 1:", info1)
 
     converted_sd, logit_scale = convert_sdxl_text_encoder_2_checkpoint(te2_sd, max_length=77)

library/sdxl_train_util.py

@@ -37,7 +37,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
                 model_version,
                 weight_dtype,
                 accelerator.device if args.lowram else "cpu",
-                model_dtype
+                model_dtype,
             )
 
             # work on low-ram device
@@ -56,7 +56,9 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype):
     return load_stable_diffusion_format, text_encoder1, text_encoder2, vae, unet, logit_scale, ckpt_info
 
 
-def _load_target_model(name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None):
+def _load_target_model(
+    name_or_path: str, vae_path: Optional[str], model_version: str, weight_dtype, device="cpu", model_dtype=None
+):
     # model_dtype only work with full fp16/bf16
     name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
     load_stable_diffusion_format = os.path.isfile(name_or_path)  # determine SD or Diffusers

library/train_util.py

@@ -2898,7 +2898,8 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         "--ip_noise_gamma",
         type=float,
         default=None,
-        help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) /  ",
+        help="enable input perturbation noise. used for regularization. recommended value: around 0.1 (from arxiv.org/abs/2301.11706) "
+        + "/  input perturbation noiseを有効にする。正則化に使用される。推奨値: 0.1程度 (arxiv.org/abs/2301.11706 より)",
     )
     # parser.add_argument(
     #     "--perlin_noise",
@@ -4353,11 +4354,11 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     timesteps = torch.randint(min_timestep, max_timestep, (b_size,), device=latents.device)
     timesteps = timesteps.long()
 
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
     if args.ip_noise_gamma:
         noisy_latents = noise_scheduler.add_noise(latents, noise + args.ip_noise_gamma * torch.randn_like(latents), timesteps)
     else:
-        # Add noise to the latents according to the noise magnitude at each timestep
-        # (this is the forward diffusion process)
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
     return noise, noisy_latents, timesteps

networks/control_net_lllite_for_train.py

@@ -1,4 +1,4 @@
-# cond_imageをU-Netのforardで渡すバージョンのControlNet-LLLite検証用実装
+# cond_imageをU-Netのforwardで渡すバージョンのControlNet-LLLite検証用実装
 # ControlNet-LLLite implementation for verification with cond_image passed in U-Net's forward
 
 import os

sdxl_train_control_net_lllite_alt.py

@@ -1,4 +1,4 @@
-# cond_imageをU-Netのforardで渡すバージョンのControlNet-LLLite検証用学習コード
+# cond_imageをU-Netのforwardで渡すバージョンのControlNet-LLLite検証用学習コード
 # training code for ControlNet-LLLite with passing cond_image to U-Net's forward
 
 import argparse