Merge branch 'sdxl' of https://github.com/kohya-ss/sd-scripts into dev2

README.md

@@ -76,7 +76,13 @@ First SDXL Tutorial: [First Ever SDXL Training With Kohya LoRA - Stable Diffusio
 
 The feature of SDXL training is now available in sdxl branch as an experimental feature. 
 
-Aug 6, 2023: The feature will be merged into the main branch soon. Following are the changes from the previous version.
+Aug 12, 2023: The feature will be merged into the main branch soon. Following are the changes from the previous version. 
+
+- The default value of noise offset when omitted has been changed to 0 from 0.0357.
+- The different learning rates for each U-Net block are now supported. Specify with `--block_lr` option. Specify 23 values separated by commas like `--block_lr 1e-3,1e-3 ... 1e-3`.
+  - 23 values correspond to `0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out`.
+
+Aug 6, 2023: 
 
 - [SAI Model Spec](https://github.com/Stability-AI/ModelSpec) metadata is now supported partially. `hash_sha256` is not supported yet.
   - The main items are set automatically. 

finetune/prepare_buckets_latents.py

@@ -52,6 +52,10 @@ def main(args):
     # assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります"
     if args.bucket_reso_steps % 8 > 0:
         print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります")
+    if args.bucket_reso_steps % 32 > 0:
+        print(
+            f"WARNING: bucket_reso_steps is not divisible by 32. It is not working with SDXL / bucket_reso_stepsが32で割り切れません。SDXLでは動作しません"
+        )
 
     train_data_dir_path = Path(args.train_data_dir)
     image_paths: List[str] = [str(p) for p in train_util.glob_images_pathlib(train_data_dir_path, args.recursive)]

library/sdxl_train_util.py

@@ -13,7 +13,7 @@
 TOKENIZER1_PATH = "openai/clip-vit-large-patch14"
 TOKENIZER2_PATH = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
 
-DEFAULT_NOISE_OFFSET = 0.0357
+# DEFAULT_NOISE_OFFSET = 0.0357
 
 
 def load_target_model(args, accelerator, model_version: str, weight_dtype):
@@ -312,18 +312,18 @@ def verify_sdxl_training_args(args: argparse.Namespace, supportTextEncoderCachin
     if args.clip_skip is not None:
         print("clip_skip will be unexpected / SDXL学習ではclip_skipは動作しません")
 
-    if args.multires_noise_iterations:
-        print(
-            f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET}, but noise_offset is disabled due to multires_noise_iterations / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されていますが、multires_noise_iterationsが有効になっているためnoise_offsetは無効になります"
-        )
-    else:
-        if args.noise_offset is None:
-            args.noise_offset = DEFAULT_NOISE_OFFSET
-        elif args.noise_offset != DEFAULT_NOISE_OFFSET:
-            print(
-                f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET} / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されています"
-            )
-        print(f"noise_offset is set to {args.noise_offset} / noise_offsetが{args.noise_offset}に設定されました")
+    # if args.multires_noise_iterations:
+    #     print(
+    #         f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET}, but noise_offset is disabled due to multires_noise_iterations / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されていますが、multires_noise_iterationsが有効になっているためnoise_offsetは無効になります"
+    #     )
+    # else:
+    #     if args.noise_offset is None:
+    #         args.noise_offset = DEFAULT_NOISE_OFFSET
+    #     elif args.noise_offset != DEFAULT_NOISE_OFFSET:
+    #         print(
+    #             f"Warning: SDXL has been trained with noise_offset={DEFAULT_NOISE_OFFSET} / SDXLはnoise_offset={DEFAULT_NOISE_OFFSET}で学習されています"
+    #         )
+    #     print(f"noise_offset is set to {args.noise_offset} / noise_offsetが{args.noise_offset}に設定されました")
 
     assert (
         not hasattr(args, "weighted_captions") or not args.weighted_captions

library/train_util.py

@@ -800,6 +800,12 @@ def shuffle_buckets(self):
         random.shuffle(self.buckets_indices)
         self.bucket_manager.shuffle()
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        assert self.bucket_reso_steps is None or self.bucket_reso_steps % min_steps == 0, (
+            f"bucket_reso_steps is {self.bucket_reso_steps}. it must be divisible by {min_steps}.\n"
+            + f"bucket_reso_stepsが{self.bucket_reso_steps}です。{min_steps}で割り切れる必要があります"
+        )
+
     def is_latent_cacheable(self):
         return all([not subset.color_aug and not subset.random_crop for subset in self.subsets])
 
@@ -1831,6 +1837,10 @@ def set_caching_mode(self, caching_mode):
         for dataset in self.datasets:
             dataset.set_caching_mode(caching_mode)
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        for dataset in self.datasets:
+            dataset.verify_bucket_reso_steps(min_steps)
+
     def is_latent_cacheable(self) -> bool:
         return all([dataset.is_latent_cacheable() for dataset in self.datasets])
 
@@ -2020,6 +2030,9 @@ def __init__(self, tokenizer, max_token_length, resolution, debug_dataset=False)
         self.is_reg = False
         self.image_dir = "dummy"  # for metadata
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        pass
+
     def is_latent_cacheable(self) -> bool:
         return False
 
@@ -2981,11 +2994,11 @@ def verify_training_args(args: argparse.Namespace):
         )
 
     # noise_offset, perlin_noise, multires_noise_iterations cannot be enabled at the same time
-    # Listを使って数えてもいいけど並べてしまえ
-    if args.noise_offset is not None and args.multires_noise_iterations is not None:
-        raise ValueError(
-            "noise_offset and multires_noise_iterations cannot be enabled at the same time / noise_offsetとmultires_noise_iterationsを同時に有効にできません"
-        )
+    # # Listを使って数えてもいいけど並べてしまえ
+    # if args.noise_offset is not None and args.multires_noise_iterations is not None:
+    #     raise ValueError(
+    #         "noise_offset and multires_noise_iterations cannot be enabled at the same time / noise_offsetとmultires_noise_iterationsを同時に有効にできません"
+    #     )
     # if args.noise_offset is not None and args.perlin_noise is not None:
     #     raise ValueError("noise_offset and perlin_noise cannot be enabled at the same time / noise_offsetとperlin_noiseは同時に有効にできません")
     # if args.perlin_noise is not None and args.multires_noise_iterations is not None:
@@ -4268,7 +4281,7 @@ def get_noise_noisy_latents_and_timesteps(args, noise_scheduler, latents):
     noise = torch.randn_like(latents, device=latents.device)
     if args.noise_offset:
         noise = custom_train_functions.apply_noise_offset(latents, noise, args.noise_offset, args.adaptive_noise_scale)
-    elif args.multires_noise_iterations:
+    if args.multires_noise_iterations:
         noise = custom_train_functions.pyramid_noise_like(
             noise, latents.device, args.multires_noise_iterations, args.multires_noise_discount
         )

sdxl_gen_img.py

@@ -1309,7 +1309,10 @@ def main(args):
 
     # schedulerを用意する
     sched_init_args = {}
+    has_steps_offset = True
+    has_clip_sample = True
     scheduler_num_noises_per_step = 1
+
     if args.sampler == "ddim":
         scheduler_cls = DDIMScheduler
         scheduler_module = diffusers.schedulers.scheduling_ddim
@@ -1319,32 +1322,48 @@ def main(args):
     elif args.sampler == "pndm":
         scheduler_cls = PNDMScheduler
         scheduler_module = diffusers.schedulers.scheduling_pndm
+        has_clip_sample = False
     elif args.sampler == "lms" or args.sampler == "k_lms":
         scheduler_cls = LMSDiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_lms_discrete
+        has_clip_sample = False
     elif args.sampler == "euler" or args.sampler == "k_euler":
         scheduler_cls = EulerDiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_euler_discrete
+        has_clip_sample = False
     elif args.sampler == "euler_a" or args.sampler == "k_euler_a":
         scheduler_cls = EulerAncestralDiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_euler_ancestral_discrete
+        has_clip_sample = False
     elif args.sampler == "dpmsolver" or args.sampler == "dpmsolver++":
         scheduler_cls = DPMSolverMultistepScheduler
         sched_init_args["algorithm_type"] = args.sampler
         scheduler_module = diffusers.schedulers.scheduling_dpmsolver_multistep
+        has_clip_sample = False
     elif args.sampler == "dpmsingle":
         scheduler_cls = DPMSolverSinglestepScheduler
         scheduler_module = diffusers.schedulers.scheduling_dpmsolver_singlestep
+        has_clip_sample = False
+        has_steps_offset = False
     elif args.sampler == "heun":
         scheduler_cls = HeunDiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_heun_discrete
+        has_clip_sample = False
     elif args.sampler == "dpm_2" or args.sampler == "k_dpm_2":
         scheduler_cls = KDPM2DiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_discrete
+        has_clip_sample = False
     elif args.sampler == "dpm_2_a" or args.sampler == "k_dpm_2_a":
         scheduler_cls = KDPM2AncestralDiscreteScheduler
         scheduler_module = diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete
         scheduler_num_noises_per_step = 2
+        has_clip_sample = False
+
+    # 警告を出さないようにする
+    if has_steps_offset:
+        sched_init_args["steps_offset"] = 1
+    if has_clip_sample:
+        sched_init_args["clip_sample"] = False
 
     # samplerの乱数をあらかじめ指定するための処理
 
@@ -1397,10 +1416,11 @@ def __getattr__(self, item):
         **sched_init_args,
     )
 
-    # clip_sample=Trueにする
-    if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
-        print("set clip_sample to True")
-        scheduler.config.clip_sample = True
+    # ↓以下は結局PipeでFalseに設定されるので意味がなかった
+    # # clip_sample=Trueにする
+    # if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is False:
+    #     print("set clip_sample to True")
+    #     scheduler.config.clip_sample = True
 
     # deviceを決定する
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # "mps"を考量してない

sdxl_train.py

@@ -5,6 +5,7 @@
 import math
 import os
 from multiprocessing import Value
+from typing import List
 import toml
 
 from tqdm import tqdm
@@ -30,6 +31,67 @@
 from library.sdxl_original_unet import SdxlUNet2DConditionModel
 
 
+UNET_NUM_BLOCKS_FOR_BLOCK_LR = 23
+
+
+def get_block_params_to_optimize(unet: SdxlUNet2DConditionModel, block_lrs: List[float]) -> List[dict]:
+    block_params = [[] for _ in range(len(block_lrs))]
+
+    for i, (name, param) in enumerate(unet.named_parameters()):
+        if name.startswith("time_embed.") or name.startswith("label_emb."):
+            block_index = 0  # 0
+        elif name.startswith("input_blocks."):  # 1-9
+            block_index = 1 + int(name.split(".")[1])
+        elif name.startswith("middle_block."):  # 10-12
+            block_index = 10 + int(name.split(".")[1])
+        elif name.startswith("output_blocks."):  # 13-21
+            block_index = 13 + int(name.split(".")[1])
+        elif name.startswith("out."):  # 22
+            block_index = 22
+        else:
+            raise ValueError(f"unexpected parameter name: {name}")
+
+        block_params[block_index].append(param)
+
+    params_to_optimize = []
+    for i, params in enumerate(block_params):
+        if block_lrs[i] == 0:  # 0のときは学習しない do not optimize when lr is 0
+            continue
+        params_to_optimize.append({"params": params, "lr": block_lrs[i]})
+
+    return params_to_optimize
+
+
+def append_block_lr_to_logs(block_lrs, logs, lr_scheduler, optimizer_type):
+    lrs = lr_scheduler.get_last_lr()
+
+    lr_index = 0
+    block_index = 0
+    while lr_index < len(lrs):
+        if block_index < UNET_NUM_BLOCKS_FOR_BLOCK_LR:
+            name = f"block{block_index}"
+            if block_lrs[block_index] == 0:
+                block_index += 1
+                continue
+        elif block_index == UNET_NUM_BLOCKS_FOR_BLOCK_LR:
+            name = "text_encoder1"
+        elif block_index == UNET_NUM_BLOCKS_FOR_BLOCK_LR + 1:
+            name = "text_encoder2"
+        else:
+            raise ValueError(f"unexpected block_index: {block_index}")
+
+        block_index += 1
+
+        logs["lr/" + name] = float(lrs[lr_index])
+
+        if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower() == "Prodigy".lower():
+            logs["lr/d*lr/" + name] = (
+                lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"]
+            )
+
+        lr_index += 1
+
+
 def train(args):
     train_util.verify_training_args(args)
     train_util.prepare_dataset_args(args, True)
@@ -40,6 +102,14 @@ def train(args):
         not args.train_text_encoder or not args.cache_text_encoder_outputs
     ), "cache_text_encoder_outputs is not supported when training text encoder / text encoderを学習するときはcache_text_encoder_outputsはサポートされていません"
 
+    if args.block_lr:
+        block_lrs = [float(lr) for lr in args.block_lr.split(",")]
+        assert (
+            len(block_lrs) == UNET_NUM_BLOCKS_FOR_BLOCK_LR
+        ), f"block_lr must have {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / block_lrは{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値を指定してください"
+    else:
+        block_lrs = None
+
     cache_latents = args.cache_latents
     use_dreambooth_method = args.in_json is None
 
@@ -98,6 +168,8 @@ def train(args):
     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)
 
+    train_dataset_group.verify_bucket_reso_steps(32)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group, True)
         return
@@ -233,15 +305,28 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     for m in training_models:
         m.requires_grad_(True)
-    params = []
-    for m in training_models:
-        params.extend(m.parameters())
-    params_to_optimize = params
 
-    # calculate number of trainable parameters
-    n_params = 0
-    for p in params:
-        n_params += p.numel()
+    if block_lrs is None:
+        params = []
+        for m in training_models:
+            params.extend(m.parameters())
+        params_to_optimize = params
+
+        # calculate number of trainable parameters
+        n_params = 0
+        for p in params:
+            n_params += p.numel()
+    else:
+        params_to_optimize = get_block_params_to_optimize(training_models[0], block_lrs)  # U-Net
+        for m in training_models[1:]:  # Text Encoders if exists
+            params_to_optimize.append({"params": m.parameters(), "lr": args.learning_rate})
+
+        # calculate number of trainable parameters
+        n_params = 0
+        for params in params_to_optimize:
+            for p in params["params"]:
+                n_params += p.numel()
+
     accelerator.print(f"number of models: {len(training_models)}")
     accelerator.print(f"number of trainable parameters: {n_params}")
 
@@ -526,13 +611,18 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
             current_loss = loss.detach().item()  # 平均なのでbatch sizeは関係ないはず
             if args.logging_dir is not None:
-                logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
-                if (
-                    args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy"
-                ):  # tracking d*lr value
-                    logs["lr/d*lr"] = (
-                        lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
-                    )
+                logs = {"loss": current_loss}
+                if block_lrs is None:
+                    logs["lr"] = float(lr_scheduler.get_last_lr()[0])
+                    if (
+                        args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
+                    ):  # tracking d*lr value
+                        logs["lr/d*lr"] = (
+                            lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
+                        )
+                else:
+                    append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type)
+
                 accelerator.log(logs, step=global_step)
 
             # TODO moving averageにする
@@ -636,6 +726,13 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
     )
+    parser.add_argument(
+        "--block_lr",
+        type=str,
+        default=None,
+        help=f"learning rates for each block of U-Net, comma-separated, {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / "
+        + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値",
+    )
 
     return parser
 

sdxl_train_network.py

@@ -23,6 +23,8 @@ def assert_extra_args(self, args, train_dataset_group):
             args.network_train_unet_only or not args.cache_text_encoder_outputs
         ), "network for Text Encoder cannot be trained with caching Text Encoder outputs / Text Encoderの出力をキャッシュしながらText Encoderのネットワークを学習することはできません"
 
+        train_dataset_group.verify_bucket_reso_steps(32)
+
     def load_target_model(self, args, weight_dtype, accelerator):
         (
             load_stable_diffusion_format,

sdxl_train_textual_inversion.py

@@ -19,6 +19,8 @@ def assert_extra_args(self, args, train_dataset_group):
         super().assert_extra_args(args, train_dataset_group)
         sdxl_train_util.verify_sdxl_training_args(args, supportTextEncoderCaching=False)
 
+        train_dataset_group.verify_bucket_reso_steps(32)
+
     def load_target_model(self, args, weight_dtype, accelerator):
         (
             load_stable_diffusion_format,