Fixed SNR weighting, re-enabled xformers

1 files changed, 59 insertions, 11 deletions

diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index cab5e4c..aa75bec 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -2,6 +2,7 @@ from typing import Optional
 from functools import partial
 from contextlib import contextmanager
 from pathlib import Path
+import itertools
 
 import torch
 from torch.utils.data import DataLoader
@@ -9,12 +10,18 @@ from torch.utils.data import DataLoader
 from accelerate import Accelerator
 from transformers import CLIPTextModel
 from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
-from diffusers.loaders import AttnProcsLayers
+from peft import LoraConfig, LoraModel, get_peft_model_state_dict
+from peft.tuners.lora import mark_only_lora_as_trainable
 
 from models.clip.tokenizer import MultiCLIPTokenizer
 from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
 
 
+# https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py
+UNET_TARGET_MODULES = ["to_q", "to_v", "query", "value"]
+TEXT_ENCODER_TARGET_MODULES = ["q_proj", "v_proj"]
+
+
 def lora_strategy_callbacks(
     accelerator: Accelerator,
     unet: UNet2DConditionModel,
@@ -27,7 +34,6 @@ def lora_strategy_callbacks(
     sample_output_dir: Path,
     checkpoint_output_dir: Path,
     seed: int,
-    lora_layers: AttnProcsLayers,
     max_grad_norm: float = 1.0,
     sample_batch_size: int = 1,
     sample_num_batches: int = 1,
@@ -57,7 +63,8 @@ def lora_strategy_callbacks(
     )
 
     def on_prepare():
-        lora_layers.requires_grad_(True)
+        mark_only_lora_as_trainable(unet.model, unet.peft_config.bias)
+        mark_only_lora_as_trainable(text_encoder.model, text_encoder.peft_config.bias)
 
     def on_accum_model():
         return unet
@@ -73,24 +80,44 @@ def lora_strategy_callbacks(
         yield
 
     def on_before_optimize(lr: float, epoch: int):
-        accelerator.clip_grad_norm_(lora_layers.parameters(), max_grad_norm)
+        accelerator.clip_grad_norm_(
+            itertools.chain(unet.parameters(), text_encoder.parameters()),
+            max_grad_norm
+        )
 
     @torch.no_grad()
     def on_checkpoint(step, postfix):
         print(f"Saving checkpoint for step {step}...")
 
         unet_ = accelerator.unwrap_model(unet, False)
-        unet_.save_attn_procs(
-            checkpoint_output_dir / f"{step}_{postfix}",
-            safe_serialization=True
+        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+
+        lora_config = {}
+        state_dict = get_peft_model_state_dict(unet, state_dict=accelerator.get_state_dict(unet))
+        lora_config["peft_config"] = unet.get_peft_config_as_dict(inference=True)
+
+        text_encoder_state_dict = get_peft_model_state_dict(
+            text_encoder, state_dict=accelerator.get_state_dict(text_encoder)
         )
+        text_encoder_state_dict = {f"text_encoder_{k}": v for k, v in text_encoder_state_dict.items()}
+        state_dict.update(text_encoder_state_dict)
+        lora_config["text_encoder_peft_config"] = text_encoder.get_peft_config_as_dict(inference=True)
+
+        accelerator.print(state_dict)
+        accelerator.save(state_dict, checkpoint_output_dir / f"{step}_{postfix}.pt")
+
         del unet_
+        del text_encoder_
 
     @torch.no_grad()
     def on_sample(step):
         unet_ = accelerator.unwrap_model(unet, False)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+
         save_samples_(step=step, unet=unet_)
+
         del unet_
+        del text_encoder_
 
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -114,13 +141,34 @@ def lora_prepare(
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
-    lora_layers: AttnProcsLayers,
+    lora_rank: int = 4,
+    lora_alpha: int = 32,
+    lora_dropout: float = 0,
+    lora_bias: str = "none",
     **kwargs
 ):
-    lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
-        lora_layers, optimizer, train_dataloader, val_dataloader, lr_scheduler)
+    unet_config = LoraConfig(
+        r=lora_rank,
+        lora_alpha=lora_alpha,
+        target_modules=UNET_TARGET_MODULES,
+        lora_dropout=lora_dropout,
+        bias=lora_bias,
+    )
+    unet = LoraModel(unet_config, unet)
+
+    text_encoder_config = LoraConfig(
+        r=lora_rank,
+        lora_alpha=lora_alpha,
+        target_modules=TEXT_ENCODER_TARGET_MODULES,
+        lora_dropout=lora_dropout,
+        bias=lora_bias,
+    )
+    text_encoder = LoraModel(text_encoder_config, text_encoder)
+
+    text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler)
 
-    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {"lora_layers": lora_layers}
+    return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
 
 
 lora_strategy = TrainingStrategy(