v12 release

README.md

@@ -390,4 +390,8 @@ options:
     - Fixed a bug where prior_loss_weight was applied to learning images. Sorry for the inconvenience.
     - Compatible with Stable Diffusion v2.0. Add the `--v2` option. If you are using `768-v-ema.ckpt` or `stable-diffusion-2` instead of `stable-diffusion-v2-base`, add `--v_parameterization` as well. Learn more about other options.
     - Added options related to the learning rate scheduler.
-    - You can download and use DiffUsers models directly from Hugging Face. In addition, DiffUsers models can be saved during training.
+    - You can download and use DiffUsers models directly from Hugging Face. In addition, DiffUsers models can be saved during training.
+* 11/29 (v12) update:
+    - stop training text encoder at specified step (`--stop_text_encoder_training=<step #>`)
+    - tqdm smoothing
+    - updated fine tuning script to support SD2.0 768/v

train_db_fixed.py

@@ -6,10 +6,11 @@
 # v8: supports Diffusers 0.7.2
 # v9: add bucketing option
 # v10: add min_bucket_reso/max_bucket_reso options, read captions for train/reg images in DreamBooth
-# v11: Diffusers 0.9.0 is required. support for Stable Diffusion 2.0/v-parameterization 
+# v11: Diffusers 0.9.0 is required. support for Stable Diffusion 2.0/v-parameterization
 #      add lr scheduler options, change handling folder/file caption, support loading DiffUser model from Huggingface
 #      support save_ever_n_epochs/save_state in DiffUsers model
 #      fix the issue that prior_loss_weight is applyed to train images
+# v12: stop train text encode, tqdm smoothing
 
 import time
 from torch.autograd.function import Function
@@ -39,33 +40,6 @@
 TOKENIZER_PATH = "openai/clip-vit-large-patch14"
 V2_STABLE_DIFFUSION_PATH = "stabilityai/stable-diffusion-2"     # ここからtokenizerだけ使う
 
-# DiffUsers版StableDiffusionのモデルパラメータ
-NUM_TRAIN_TIMESTEPS = 1000
-BETA_START = 0.00085
-BETA_END = 0.0120
-
-UNET_PARAMS_MODEL_CHANNELS = 320
-UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
-UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
-UNET_PARAMS_IMAGE_SIZE = 32  # unused
-UNET_PARAMS_IN_CHANNELS = 4
-UNET_PARAMS_OUT_CHANNELS = 4
-UNET_PARAMS_NUM_RES_BLOCKS = 2
-UNET_PARAMS_CONTEXT_DIM = 768
-UNET_PARAMS_NUM_HEADS = 8
-
-VAE_PARAMS_Z_CHANNELS = 4
-VAE_PARAMS_RESOLUTION = 256
-VAE_PARAMS_IN_CHANNELS = 3
-VAE_PARAMS_OUT_CH = 3
-VAE_PARAMS_CH = 128
-VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
-VAE_PARAMS_NUM_RES_BLOCKS = 2
-
-# V2
-V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
-V2_UNET_PARAMS_CONTEXT_DIM = 1024
-
 # checkpointファイル名
 LAST_CHECKPOINT_NAME = "last.ckpt"
 LAST_STATE_NAME = "last-state"
@@ -693,6 +667,34 @@ def forward_xformers(self, x, context=None, mask=None):
 
 # region checkpoint変換、読み込み、書き込み ###############################
 
+# DiffUsers版StableDiffusionのモデルパラメータ
+NUM_TRAIN_TIMESTEPS = 1000
+BETA_START = 0.00085
+BETA_END = 0.0120
+
+UNET_PARAMS_MODEL_CHANNELS = 320
+UNET_PARAMS_CHANNEL_MULT = [1, 2, 4, 4]
+UNET_PARAMS_ATTENTION_RESOLUTIONS = [4, 2, 1]
+UNET_PARAMS_IMAGE_SIZE = 32  # unused
+UNET_PARAMS_IN_CHANNELS = 4
+UNET_PARAMS_OUT_CHANNELS = 4
+UNET_PARAMS_NUM_RES_BLOCKS = 2
+UNET_PARAMS_CONTEXT_DIM = 768
+UNET_PARAMS_NUM_HEADS = 8
+
+VAE_PARAMS_Z_CHANNELS = 4
+VAE_PARAMS_RESOLUTION = 256
+VAE_PARAMS_IN_CHANNELS = 3
+VAE_PARAMS_OUT_CH = 3
+VAE_PARAMS_CH = 128
+VAE_PARAMS_CH_MULT = [1, 2, 4, 4]
+VAE_PARAMS_NUM_RES_BLOCKS = 2
+
+# V2
+V2_UNET_PARAMS_ATTENTION_HEAD_DIM = [5, 10, 20, 20]
+V2_UNET_PARAMS_CONTEXT_DIM = 1024
+
+
 # region StableDiffusion->Diffusersの変換コード
 # convert_original_stable_diffusion_to_diffusers をコピーしている（ASL 2.0）
 
@@ -1408,9 +1410,13 @@ def load_checkpoint_with_text_encoder_conversion(ckpt_path):
   return checkpoint
 
 
-def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path):
+def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
   checkpoint = load_checkpoint_with_text_encoder_conversion(ckpt_path)
   state_dict = checkpoint["state_dict"]
+  if dtype is not None:
+    for k, v in state_dict.items():
+      if type(v) is torch.Tensor:
+        state_dict[k] = v.to(dtype)
 
   # Convert the UNet2DConditionModel model.
   unet_config = create_unet_diffusers_config(v2)
@@ -1854,10 +1860,15 @@ def load_dreambooth_dir(dir):
   print(f"  total train batch size (with parallel & distributed) / 総バッチサイズ（並列学習含む）: {total_batch_size}")
   print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
-  progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process, desc="steps")
+  progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
   global_step = 0
 
-  noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+  # v12で更新：clip_sample=Falseに
+  # Diffusersのtrain_dreambooth.pyがconfigから持ってくるように変更されたので、clip_sample=Falseになるため、それに合わせる
+  # 既存の1.4/1.5/2.0はすべてschdulerのconfigは（クラス名を除いて）同じ
+  # よくソースを見たら学習時は関係ないや(;'∀')　
+  noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
+                                  num_train_timesteps=1000, clip_sample=False)
 
   if accelerator.is_main_process:
     accelerator.init_trackers("dreambooth")
@@ -1891,13 +1902,16 @@ def load_dreambooth_dir(dir):
         # (this is the forward diffusion process)
         noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
 
-        # Get the text embedding for conditioning
-        if args.clip_skip is None:
-          encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-        else:
-          enc_out = text_encoder(batch["input_ids"], output_hidden_states=True, return_dict=True)
-          encoder_hidden_states = enc_out['hidden_states'][-args.clip_skip]
-          encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states)
+        # 指定したステップ数までText Encoderを学習する
+        train_text_encoder = args.stop_text_encoder_training is None or global_step < args.stop_text_encoder_training 
+        with torch.set_grad_enabled(train_text_encoder):
+          # Get the text embedding for conditioning
+          if args.clip_skip is None:
+            encoder_hidden_states = text_encoder(batch["input_ids"])[0]
+          else:
+            enc_out = text_encoder(batch["input_ids"], output_hidden_states=True, return_dict=True)
+            encoder_hidden_states = enc_out['hidden_states'][-args.clip_skip]
+            encoder_hidden_states = text_encoder.text_model.final_layer_norm(encoder_hidden_states)
 
         # Predict the noise residual
         noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
@@ -1954,6 +1968,9 @@ def load_dreambooth_dir(dir):
         progress_bar.update(1)
         global_step += 1
 
+        if global_step == args.stop_text_encoder_training:
+          print(f"stop text encoder training at step {global_step}")
+
       current_loss = loss.detach().item()
       if args.logging_dir is not None:
         logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
@@ -2052,6 +2069,7 @@ def load_dreambooth_dir(dir):
   parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="loss weight for regularization images / 正則化画像のlossの重み")
   parser.add_argument("--no_token_padding", action="store_true",
                       help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）")
+  parser.add_argument("--stop_text_encoder_training", type=int, default=None, help="steps to stop text encoder training / Text Encoderの学習を止めるステップ数")
   parser.add_argument("--color_aug", action="store_true", help="enable weak color augmentation / 学習時に色合いのaugmentationを有効にする")
   parser.add_argument("--flip_aug", action="store_true", help="enable horizontal flip augmentation / 学習時に左右反転のaugmentationを有効にする")
   parser.add_argument("--face_crop_aug_range", type=str, default=None,