Merge branch 'original-u-net' into sdxl

library/original_unet.py

@@ -1468,6 +1468,8 @@ class UNet2DConditionModel(nn.Module):
         encoder_hidden_states: torch.Tensor,
         class_labels: Optional[torch.Tensor] = None,
         return_dict: bool = True,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
     ) -> Union[Dict, Tuple]:
         r"""
         Args:
@@ -1533,9 +1535,20 @@ class UNet2DConditionModel(nn.Module):
 
             down_block_res_samples += res_samples
 
+        # skip connectionにControlNetの出力を追加する
+        if down_block_additional_residuals is not None:
+            down_block_res_samples = list(down_block_res_samples)
+            for i in range(len(down_block_res_samples)):
+                down_block_res_samples[i] += down_block_additional_residuals[i]
+            down_block_res_samples = tuple(down_block_res_samples)
+
         # 4. mid
         sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
 
+        # ControlNetの出力を追加する
+        if mid_block_additional_residual is not None:
+            sample += mid_block_additional_residual
+
         # 5. up
         for i, upsample_block in enumerate(self.up_blocks):
             is_final_block = i == len(self.up_blocks) - 1

train_controlnet.py

@@ -6,6 +6,7 @@ import os
 import random
 import time
 from multiprocessing import Value
+from types import SimpleNamespace
 
 from tqdm import tqdm
 import torch
@@ -39,17 +40,14 @@ def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_sche
     }
 
     if args.optimizer_type.lower().startswith("DAdapt".lower()):
-        logs["lr/d*lr"] = (
-            lr_scheduler.optimizers[-1].param_groups[0]["d"]
-            * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
-        )
+        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
 
     return logs
 
 
 def train(args):
-    session_id = random.randint(0, 2**32)
-    training_started_at = time.time()
+    # session_id = random.randint(0, 2**32)
+    # training_started_at = time.time()
     train_util.verify_training_args(args)
     train_util.prepare_dataset_args(args, True)
 
@@ -88,15 +86,11 @@ def train(args):
         }
 
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
-    train_dataset_group = config_util.generate_dataset_group_by_blueprint(
-        blueprint.dataset_group
-    )
+    train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
     current_epoch = Value("i", 0)
     current_step = Value("i", 0)
-    ds_for_collater = (
-        train_dataset_group if args.max_data_loader_n_workers == 0 else None
-    )
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
     collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
 
     if args.debug_dataset:
@@ -115,7 +109,7 @@ def train(args):
 
     # acceleratorを準備する
     print("prepare accelerator")
-    accelerator, unwrap_model = train_util.prepare_accelerator(args)
+    accelerator = train_util.prepare_accelerator(args)
     is_main_process = accelerator.is_main_process
 
     # mixed precisionに対応した型を用意しておき適宜castする
@@ -126,6 +120,69 @@ def train(args):
         args, weight_dtype, accelerator, unet_use_linear_projection_in_v2=True
     )
 
+    # DiffusersのControlNetが使用するデータを準備する
+    if args.v2:
+        unet.config = {
+            "act_fn": "silu",
+            "attention_head_dim": [5, 10, 20, 20],
+            "block_out_channels": [320, 640, 1280, 1280],
+            "center_input_sample": False,
+            "cross_attention_dim": 1024,
+            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
+            "downsample_padding": 1,
+            "dual_cross_attention": False,
+            "flip_sin_to_cos": True,
+            "freq_shift": 0,
+            "in_channels": 4,
+            "layers_per_block": 2,
+            "mid_block_scale_factor": 1,
+            "norm_eps": 1e-05,
+            "norm_num_groups": 32,
+            "num_class_embeds": None,
+            "only_cross_attention": False,
+            "out_channels": 4,
+            "sample_size": 96,
+            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
+            "use_linear_projection": True,
+            "upcast_attention": True,
+            "only_cross_attention": False,
+            "downsample_padding": 1,
+            "use_linear_projection": True,
+            "class_embed_type": None,
+            "num_class_embeds": None,
+            "resnet_time_scale_shift": "default",
+            "projection_class_embeddings_input_dim": None,
+        }
+    else:
+        unet.config = {
+            "act_fn": "silu",
+            "attention_head_dim": 8,
+            "block_out_channels": [320, 640, 1280, 1280],
+            "center_input_sample": False,
+            "cross_attention_dim": 768,
+            "down_block_types": ["CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"],
+            "downsample_padding": 1,
+            "flip_sin_to_cos": True,
+            "freq_shift": 0,
+            "in_channels": 4,
+            "layers_per_block": 2,
+            "mid_block_scale_factor": 1,
+            "norm_eps": 1e-05,
+            "norm_num_groups": 32,
+            "out_channels": 4,
+            "sample_size": 64,
+            "up_block_types": ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"],
+            "only_cross_attention": False,
+            "downsample_padding": 1,
+            "use_linear_projection": False,
+            "class_embed_type": None,
+            "num_class_embeds": None,
+            "upcast_attention": False,
+            "resnet_time_scale_shift": "default",
+            "projection_class_embeddings_input_dim": None,
+        }
+    unet.config = SimpleNamespace(**unet.config)
+
     controlnet = ControlNetModel.from_unet(unet)
 
     if args.controlnet_model_name_or_path:
@@ -140,9 +197,8 @@ def train(args):
         elif os.path.isdir(filename):
             controlnet = ControlNetModel.from_pretrained(filename)
 
-
     # モデルに xformers とか memory efficient attention を組み込む
-    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers, args.sdpa)
 
     # 学習を準備する
     if cache_latents:
@@ -171,15 +227,11 @@ def train(args):
 
     trainable_params = controlnet.parameters()
 
-    _, _, optimizer = train_util.get_optimizer(
-        args, trainable_params
-    )
+    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
 
     # dataloaderを準備する
     # DataLoaderのプロセス数：0はメインプロセスになる
-    n_workers = min(
-        args.max_data_loader_n_workers, os.cpu_count() - 1
-    )  # cpu_count-1 ただし最大で指定された数まで
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
 
     train_dataloader = torch.utils.data.DataLoader(
         train_dataset_group,
@@ -193,21 +245,15 @@ def train(args):
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
         args.max_train_steps = args.max_train_epochs * math.ceil(
-            len(train_dataloader)
-            / accelerator.num_processes
-            / args.gradient_accumulation_steps
-        )
-        accelerator.print(
-            f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}"
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
         )
+        accelerator.print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
     # データセット側にも学習ステップを送信
     train_dataset_group.set_max_train_steps(args.max_train_steps)
 
     # lr schedulerを用意する
-    lr_scheduler = train_util.get_scheduler_fix(
-        args, optimizer, accelerator.num_processes
-    )
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
     # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
     if args.full_fp16:
@@ -245,31 +291,21 @@ def train(args):
     train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     # epoch数を計算する
-    num_update_steps_per_epoch = math.ceil(
-        len(train_dataloader) / args.gradient_accumulation_steps
-    )
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
     if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
-        args.save_every_n_epochs = (
-            math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
-        )
+        args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
 
     # 学習する
     # TODO: find a way to handle total batch size when there are multiple datasets
     accelerator.print("running training / 学習開始")
-    accelerator.print(
-        f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}"
-    )
+    accelerator.print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
     accelerator.print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
     accelerator.print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
     accelerator.print(f"  num epochs / epoch数: {num_train_epochs}")
-    accelerator.print(
-        f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}"
-    )
+    accelerator.print(f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
     # print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
-    accelerator.print(
-        f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}"
-    )
+    accelerator.print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
     accelerator.print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
     progress_bar = tqdm(
@@ -288,11 +324,7 @@ def train(args):
         clip_sample=False,
     )
     if accelerator.is_main_process:
-        accelerator.init_trackers(
-            "controlnet_train"
-            if args.log_tracker_name is None
-            else args.log_tracker_name
-        )
+        accelerator.init_trackers("controlnet_train" if args.log_tracker_name is None else args.log_tracker_name)
 
     loss_list = []
     loss_total = 0.0
@@ -321,9 +353,7 @@ def train(args):
             torch.save(state_dict, ckpt_file)
 
         if args.huggingface_repo_id is not None:
-            huggingface_util.upload(
-                args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload
-            )
+            huggingface_util.upload(args, ckpt_file, "/" + ckpt_name, force_sync_upload=force_sync_upload)
 
     def remove_model(old_ckpt_name):
         old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
@@ -345,23 +375,17 @@ def train(args):
                         latents = batch["latents"].to(accelerator.device)
                     else:
                         # latentに変換
-                        latents = vae.encode(
-                            batch["images"].to(dtype=weight_dtype)
-                        ).latent_dist.sample()
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
                     latents = latents * 0.18215
                 b_size = latents.shape[0]
 
                 input_ids = batch["input_ids"].to(accelerator.device)
-                encoder_hidden_states = train_util.get_hidden_states(
-                    args, input_ids, tokenizer, text_encoder, weight_dtype
-                )
+                encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
 
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(latents, device=latents.device)
                 if args.noise_offset:
-                    noise = apply_noise_offset(
-                        latents, noise, args.noise_offset, args.adaptive_noise_scale
-                    )
+                    noise = apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
                 elif args.multires_noise_iterations:
                     noise = pyramid_noise_like(
                         noise,
@@ -398,13 +422,8 @@ def train(args):
                         noisy_latents,
                         timesteps,
                         encoder_hidden_states,
-                        down_block_additional_residuals=[
-                            sample.to(dtype=weight_dtype)
-                            for sample in down_block_res_samples
-                        ],
-                        mid_block_additional_residual=mid_block_res_sample.to(
-                            dtype=weight_dtype
-                        ),
+                        down_block_additional_residuals=[sample.to(dtype=weight_dtype) for sample in down_block_res_samples],
+                        mid_block_additional_residual=mid_block_res_sample.to(dtype=weight_dtype),
                     ).sample
 
                 if args.v_parameterization:
@@ -413,18 +432,14 @@ def train(args):
                 else:
                     target = noise
 
-                loss = torch.nn.functional.mse_loss(
-                    noise_pred.float(), target.float(), reduction="none"
-                )
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
                 loss = loss.mean([1, 2, 3])
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                 loss = loss * loss_weights
 
                 if args.min_snr_gamma:
-                    loss = apply_snr_weight(
-                        loss, timesteps, noise_scheduler, args.min_snr_gamma
-                    )
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
@@ -456,31 +471,21 @@ def train(args):
                 )
 
                 # 指定ステップごとにモデルを保存
-                if (
-                    args.save_every_n_steps is not None
-                    and global_step % args.save_every_n_steps == 0
-                ):
+                if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0:
                     accelerator.wait_for_everyone()
                     if accelerator.is_main_process:
-                        ckpt_name = train_util.get_step_ckpt_name(
-                            args, "." + args.save_model_as, global_step
-                        )
+                        ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, global_step)
                         save_model(
-                            ckpt_name, unwrap_model(controlnet),
+                            ckpt_name,
+                            accelerator.unwrap_model(controlnet),
                         )
 
                         if args.save_state:
-                            train_util.save_and_remove_state_stepwise(
-                                args, accelerator, global_step
-                            )
+                            train_util.save_and_remove_state_stepwise(args, accelerator, global_step)
 
-                        remove_step_no = train_util.get_remove_step_no(
-                            args, global_step
-                        )
+                        remove_step_no = train_util.get_remove_step_no(args, global_step)
                         if remove_step_no is not None:
-                            remove_ckpt_name = train_util.get_step_ckpt_name(
-                                args, "." + args.save_model_as, remove_step_no
-                            )
+                            remove_ckpt_name = train_util.get_step_ckpt_name(args, "." + args.save_model_as, remove_step_no)
                             remove_model(remove_ckpt_name)
 
             current_loss = loss.detach().item()
@@ -509,26 +514,18 @@ def train(args):
 
         # 指定エポックごとにモデルを保存
         if args.save_every_n_epochs is not None:
-            saving = (epoch + 1) % args.save_every_n_epochs == 0 and (
-                epoch + 1
-            ) < num_train_epochs
+            saving = (epoch + 1) % args.save_every_n_epochs == 0 and (epoch + 1) < num_train_epochs
             if is_main_process and saving:
-                ckpt_name = train_util.get_epoch_ckpt_name(
-                    args, "." + args.save_model_as, epoch + 1
-                )
-                save_model(ckpt_name, unwrap_model(controlnet))
+                ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, epoch + 1)
+                save_model(ckpt_name, accelerator.unwrap_model(controlnet))
 
                 remove_epoch_no = train_util.get_remove_epoch_no(args, epoch + 1)
                 if remove_epoch_no is not None:
-                    remove_ckpt_name = train_util.get_epoch_ckpt_name(
-                        args, "." + args.save_model_as, remove_epoch_no
-                    )
+                    remove_ckpt_name = train_util.get_epoch_ckpt_name(args, "." + args.save_model_as, remove_epoch_no)
                     remove_model(remove_ckpt_name)
 
                 if args.save_state:
-                    train_util.save_and_remove_state_on_epoch_end(
-                        args, accelerator, epoch + 1
-                    )
+                    train_util.save_and_remove_state_on_epoch_end(args, accelerator, epoch + 1)
 
         train_util.sample_images(
             accelerator,
@@ -545,20 +542,18 @@ def train(args):
 
         # end of epoch
     if is_main_process:
-        controlnet = unwrap_model(controlnet)
+        controlnet = accelerator.unwrap_model(controlnet)
 
     accelerator.end_training()
 
     if is_main_process and args.save_state:
         train_util.save_state_on_train_end(args, accelerator)
 
-    del accelerator  # この後メモリを使うのでこれは消す
+    # del accelerator  # この後メモリを使うのでこれは消す→printで使うので消さずにおく
 
     if is_main_process:
         ckpt_name = train_util.get_last_ckpt_name(args, "." + args.save_model_as)
-        save_model(
-            ckpt_name, controlnet, force_sync_upload=True
-        )
+        save_model(ckpt_name, controlnet, force_sync_upload=True)
 
         print("model saved.")