Diff Output Preserv loss for SDXL

library/config_util.py

@@ -10,13 +10,7 @@
 from pathlib import Path
 
 # from toolz import curry
-from typing import (
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Union,
-)
+from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import toml
 import voluptuous
@@ -78,6 +72,7 @@ class BaseSubsetParams:
     caption_tag_dropout_rate: float = 0.0
     token_warmup_min: int = 1
     token_warmup_step: float = 0
+    custom_attributes: Optional[Dict[str, Any]] = None
 
 
 @dataclass
@@ -197,6 +192,7 @@ def __validate_and_convert_scalar_or_twodim(klass, value: Union[float, Sequence]
         "token_warmup_step": Any(float, int),
         "caption_prefix": str,
         "caption_suffix": str,
+        "custom_attributes": dict,
     }
     # DO means DropOut
     DO_SUBSET_ASCENDABLE_SCHEMA = {
@@ -538,9 +534,10 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
           flip_aug: {subset.flip_aug}
           face_crop_aug_range: {subset.face_crop_aug_range}
           random_crop: {subset.random_crop}
-          token_warmup_min: {subset.token_warmup_min},
-          token_warmup_step: {subset.token_warmup_step},
-          alpha_mask: {subset.alpha_mask},
+          token_warmup_min: {subset.token_warmup_min}
+          token_warmup_step: {subset.token_warmup_step}
+          alpha_mask: {subset.alpha_mask}
+          custom_attributes: {subset.custom_attributes}
       """
                 ),
                 "  ",

library/train_util.py

@@ -396,6 +396,7 @@ def __init__(
         caption_suffix: Optional[str],
         token_warmup_min: int,
         token_warmup_step: Union[float, int],
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         self.image_dir = image_dir
         self.alpha_mask = alpha_mask if alpha_mask is not None else False
@@ -419,6 +420,8 @@ def __init__(
         self.token_warmup_min = token_warmup_min  # step=0におけるタグの数
         self.token_warmup_step = token_warmup_step  # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる
 
+        self.custom_attributes = custom_attributes if custom_attributes is not None else {}
+
         self.img_count = 0
 
 
@@ -449,6 +452,7 @@ def __init__(
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -473,6 +477,7 @@ def __init__(
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
+            custom_attributes=custom_attributes,
         )
 
         self.is_reg = is_reg
@@ -512,6 +517,7 @@ def __init__(
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"
 
@@ -536,6 +542,7 @@ def __init__(
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
+            custom_attributes=custom_attributes,
         )
 
         self.metadata_file = metadata_file
@@ -1474,11 +1481,14 @@ def __getitem__(self, index):
         target_sizes_hw = []
         flippeds = []  # 変数名が微妙
         text_encoder_outputs_list = []
+        custom_attributes = []
 
         for image_key in bucket[image_index : image_index + bucket_batch_size]:
             image_info = self.image_data[image_key]
             subset = self.image_to_subset[image_key]
 
+            custom_attributes.append(subset.custom_attributes)
+
             # in case of fine tuning, is_reg is always False
             loss_weights.append(self.prior_loss_weight if image_info.is_reg else 1.0)
 
@@ -1646,7 +1656,9 @@ def none_or_stack_elements(tensors_list, converter):
                 return None
             return [torch.stack([converter(x[i]) for x in tensors_list]) for i in range(len(tensors_list[0]))]
 
+        # set example
         example = {}
+        example["custom_attributes"] = custom_attributes  # may be list of empty dict
         example["loss_weights"] = torch.FloatTensor(loss_weights)
         example["text_encoder_outputs_list"] = none_or_stack_elements(text_encoder_outputs_list, torch.FloatTensor)
         example["input_ids_list"] = none_or_stack_elements(input_ids_list, lambda x: x)
@@ -2630,7 +2642,9 @@ def debug_dataset(train_dataset, show_input_ids=False):
                     f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}", original size: {orgsz}, crop top left: {crptl}, target size: {trgsz}, flipped: {flpdz}'
                 )
                 if "network_multipliers" in example:
-                    print(f"network multiplier: {example['network_multipliers'][j]}")
+                    logger.info(f"network multiplier: {example['network_multipliers'][j]}")
+                if "custom_attributes" in example:
+                    logger.info(f"custom attributes: {example['custom_attributes'][j]}")
 
                 # if show_input_ids:
                 #     logger.info(f"input ids: {iid}")
@@ -4091,6 +4105,7 @@ def enable_high_vram(args: argparse.Namespace):
         global HIGH_VRAM
         HIGH_VRAM = True
 
+
 def verify_training_args(args: argparse.Namespace):
     r"""
     Verify training arguments. Also reflect highvram option to global variable

sdxl_train_network.py

@@ -1,4 +1,5 @@
 import argparse
+from typing import List, Optional
 
 import torch
 from accelerate import Accelerator
@@ -172,7 +173,18 @@ def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, wei
 
         return encoder_hidden_states1, encoder_hidden_states2, pool2
 
-    def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
+    def call_unet(
+        self,
+        args,
+        accelerator,
+        unet,
+        noisy_latents,
+        timesteps,
+        text_conds,
+        batch,
+        weight_dtype,
+        indices: Optional[List[int]] = None,
+    ):
         noisy_latents = noisy_latents.to(weight_dtype)  # TODO check why noisy_latents is not weight_dtype
 
         # get size embeddings
@@ -186,6 +198,12 @@ def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_cond
         vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)
         text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)
 
+        if indices is not None and len(indices) > 0:
+            noisy_latents = noisy_latents[indices]
+            timesteps = timesteps[indices]
+            text_embedding = text_embedding[indices]
+            vector_embedding = vector_embedding[indices]
+
         noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
         return noise_pred
 

train_network.py

@@ -143,7 +143,7 @@ def cache_text_encoder_outputs_if_needed(self, args, accelerator, unet, vae, tex
         for t_enc in text_encoders:
             t_enc.to(accelerator.device, dtype=weight_dtype)
 
-    def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
+    def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype, **kwargs):
         noise_pred = unet(noisy_latents, timesteps, text_conds[0]).sample
         return noise_pred
 
@@ -218,6 +218,30 @@ def get_noise_pred_and_target(
         else:
             target = noise
 
+        # differential output preservation
+        if "custom_attributes" in batch:
+            diff_output_pr_indices = []
+            for i, custom_attributes in enumerate(batch["custom_attributes"]):
+                if "diff_output_preservation" in custom_attributes and custom_attributes["diff_output_preservation"]:
+                    diff_output_pr_indices.append(i)
+
+            if len(diff_output_pr_indices) > 0:
+                network.set_multiplier(0.0)
+                with torch.no_grad(), accelerator.autocast():
+                    noise_pred_prior = self.call_unet(
+                        args,
+                        accelerator,
+                        unet,
+                        noisy_latents,
+                        timesteps,
+                        text_encoder_conds,
+                        batch,
+                        weight_dtype,
+                        indices=diff_output_pr_indices,
+                    )
+                network.set_multiplier(1.0)  # may be overwritten by "network_multipliers" in the next step
+                target[diff_output_pr_indices] = noise_pred_prior.to(target.dtype)
+
         return noise_pred, target, timesteps, huber_c, None
 
     def post_process_loss(self, loss, args, timesteps, noise_scheduler):
@@ -1123,15 +1147,6 @@ def remove_model(old_ckpt_name):
                         with torch.set_grad_enabled(train_text_encoder), accelerator.autocast():
                             # Get the text embedding for conditioning
                             if args.weighted_captions:
-                                # # SD only
-                                # encoded_text_encoder_conds = get_weighted_text_embeddings(
-                                #     tokenizers[0],
-                                #     text_encoder,
-                                #     batch["captions"],
-                                #     accelerator.device,
-                                #     args.max_token_length // 75 if args.max_token_length else 1,
-                                #     clip_skip=args.clip_skip,
-                                # )
                                 input_ids_list, weights_list = tokenize_strategy.tokenize_with_weights(batch["captions"])
                                 encoded_text_encoder_conds = text_encoding_strategy.encode_tokens_with_weights(
                                     tokenize_strategy,