Add training metadata to output LoRA model

library/train_util.py

@@ -747,6 +747,20 @@ def exists(val):
 def default(val, d):
   return val if exists(val) else d
 
+
+def model_hash(filename):
+    try:
+        with open(filename, "rb") as file:
+            import hashlib
+            m = hashlib.sha256()
+
+            file.seek(0x100000)
+            m.update(file.read(0x10000))
+            return m.hexdigest()[0:8]
+    except FileNotFoundError:
+        return 'NOFILE'
+
+
 # flash attention forwards and backwards
 
 # https://arxiv.org/abs/2205.14135

networks/extract_lora_from_models.py

@@ -135,7 +135,7 @@ def svd(args):
   if dir_name and not os.path.exists(dir_name):
     os.makedirs(dir_name, exist_ok=True)
 
-  lora_network_o.save_weights(args.save_to, save_dtype)
+  lora_network_o.save_weights(args.save_to, save_dtype, {})
   print(f"LoRA weights are saved to: {args.save_to}")
 
 

networks/lora.py

@@ -6,6 +6,8 @@
 import math
 import os
 import torch
+import zipfile
+import json
 
 
 class LoRAModule(torch.nn.Module):
@@ -61,6 +63,7 @@ class LoRANetwork(torch.nn.Module):
     super().__init__()
     self.multiplier = multiplier
     self.lora_dim = lora_dim
+    self.metadata = {}
 
     # create module instances
     def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> list[LoRAModule]:
@@ -91,11 +94,17 @@ class LoRANetwork(torch.nn.Module):
       names.add(lora.lora_name)
 
   def load_weights(self, file):
+    self.metadata = {}
     if os.path.splitext(file)[1] == '.safetensors':
       from safetensors.torch import load_file
       self.weights_sd = load_file(file)
+      self.metadata = self.weights_sd.metadata()
     else:
       self.weights_sd = torch.load(file, map_location='cpu')
+      with zipfile.ZipFile(file, "w") as zipf:
+        if "sd_scripts_metadata.json" in zipf.namelist():
+          with zipf.open("sd_scripts_metadata.json", "r") as jsfile:
+            self.metadata = json.load(jsfile)
 
   def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
     if self.weights_sd:
@@ -174,7 +183,8 @@ class LoRANetwork(torch.nn.Module):
   def get_trainable_params(self):
     return self.parameters()
 
-  def save_weights(self, file, dtype):
+  def save_weights(self, file, dtype, metadata):
+    self.metadata = metadata
     state_dict = self.state_dict()
 
     if dtype is not None:
@@ -185,6 +195,8 @@ class LoRANetwork(torch.nn.Module):
 
     if os.path.splitext(file)[1] == '.safetensors':
       from safetensors.torch import save_file
-      save_file(state_dict, file)
+      save_file(state_dict, file, metadata)
     else:
       torch.save(state_dict, file)
+      with zipfile.ZipFile(file, "w") as zipf:
+        zipf.writestr("sd_scripts_metadata.json", json.dumps(metadata))

train_network.py

@@ -197,6 +197,47 @@ def train(args):
   print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
   print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
+  metadata = {
+    "ss_learning_rate": args.learning_rate,
+    "ss_text_encoder_lr": args.text_encoder_lr,
+    "ss_unet_lr": args.unet_lr,
+    "ss_num_train_images": train_dataset.num_train_images,
+    "ss_num_reg_images": train_dataset.num_reg_images,
+    "ss_num_batches_per_epoch": len(train_dataloader),
+    "ss_num_epochs": num_train_epochs,
+    "ss_batch_size_per_device": args.train_batch_size,
+    "ss_total_batch_size": total_batch_size,
+    "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
+    "ss_max_train_steps": args.max_train_steps,
+    "ss_lr_warmup_steps": args.lr_warmup_steps,
+    "ss_lr_scheduler": args.lr_scheduler,
+    "ss_network_module": args.network_module,
+    "ss_network_dim": 4 if args.network_dim is None else args.network_dim,
+    "ss_full_fp16": bool(args.full_fp16),
+    "ss_v2": bool(args.v2),
+    "ss_resolution": args.resolution,
+    "ss_clip_skip": args.clip_skip,
+    "ss_max_token_length": args.max_token_length,
+    "ss_color_aug": bool(args.color_aug),
+    "ss_flip_aug": bool(args.flip_aug),
+    "ss_random_crop": bool(args.random_crop),
+    "ss_shuffle_caption": bool(args.shuffle_caption),
+    "ss_cache_latents": bool(args.cache_latents),
+    "ss_enable_bucket": bool(args.enable_bucket),
+    "ss_min_bucket_reso": args.min_bucket_reso,
+    "ss_max_bucket_reso": args.max_bucket_reso,
+    "ss_seed": args.seed
+  }
+
+  if args.pretrained_model_name_or_path is not None:
+    sd_model_name = args.pretrained_model_name_or_path
+    if os.path.exists(sd_model_name):
+      metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
+      sd_model_name = os.path.basename(sd_model_name)
+    metadata["ss_sd_model_name"] = sd_model_name
+
+  metadata = {k: str(v) for k, v in metadata.items()}
+
   progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
   global_step = 0
 
@@ -296,7 +337,7 @@ def train(args):
         ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + '.' + args.save_model_as
         ckpt_file = os.path.join(args.output_dir, ckpt_name)
         print(f"saving checkpoint: {ckpt_file}")
-        unwrap_model(network).save_weights(ckpt_file, save_dtype)
+        unwrap_model(network).save_weights(ckpt_file, save_dtype, metadata)
 
       def remove_old_func(old_epoch_no):
         old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + '.' + args.save_model_as
@@ -330,7 +371,7 @@ def train(args):
     ckpt_file = os.path.join(args.output_dir, ckpt_name)
 
     print(f"save trained model to {ckpt_file}")
-    network.save_weights(ckpt_file, save_dtype)
+    network.save_weights(ckpt_file, save_dtype, metadata)
     print("model saved.")