Merge branch 'main' into original-u-net

README.md

@@ -140,6 +140,13 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ## Change History
 
+### 6 Jun. 2023, 2023/06/06
+
+- Fix `train_network.py` to probably work with older versions of LyCORIS.
+- `gen_img_diffusers.py` now supports `BREAK` syntax.
+- `train_network.py`がLyCORISの以前のバージョンでも恐らく動作するよう修正しました。
+- `gen_img_diffusers.py` で `BREAK` 構文をサポートしました。
+
 ### 3 Jun. 2023, 2023/06/03
 
 - Max Norm Regularization is now available in `train_network.py`. [PR #545](https://github.com/kohya-ss/sd-scripts/pull/545) Thanks to AI-Casanova!

gen_img_diffusers.py

@@ -1801,6 +1801,9 @@ def parse_prompt_attention(text):
         for p in range(start_position, len(res)):
             res[p][1] *= multiplier
 
+    # keep break as separate token
+    text = text.replace("BREAK", "\\BREAK\\")
+
     for m in re_attention.finditer(text):
         text = m.group(0)
         weight = m.group(1)
@@ -1832,7 +1835,7 @@ def parse_prompt_attention(text):
     # merge runs of identical weights
     i = 0
     while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
+        if res[i][1] == res[i + 1][1] and res[i][0].strip() != "BREAK" and res[i + 1][0].strip() != "BREAK":
             res[i][0] += res[i + 1][0]
             res.pop(i + 1)
         else:
@@ -1849,11 +1852,25 @@ def get_prompts_with_weights(pipe: PipelineLike, prompt: List[str], max_length:
     tokens = []
     weights = []
     truncated = False
+
     for text in prompt:
         texts_and_weights = parse_prompt_attention(text)
         text_token = []
         text_weight = []
         for word, weight in texts_and_weights:
+            if word.strip() == "BREAK":
+                # pad until next multiple of tokenizer's max token length
+                pad_len = pipe.tokenizer.model_max_length - (len(text_token) % pipe.tokenizer.model_max_length)
+                print(f"BREAK pad_len: {pad_len}")
+                for i in range(pad_len):
+                    # v2のときEOSをつけるべきかどうかわからないぜ
+                    # if i == 0:
+                    #     text_token.append(pipe.tokenizer.eos_token_id)
+                    # else:
+                    text_token.append(pipe.tokenizer.pad_token_id)
+                    text_weight.append(1.0)
+                continue
+
             # tokenize and discard the starting and the ending token
             token = pipe.tokenizer(word).input_ids[1:-1]
 

networks/lora.py

@@ -400,7 +400,7 @@ def parse_block_lr_kwargs(nw_kwargs):
     return down_lr_weight, mid_lr_weight, up_lr_weight
 
 
-def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, dropout=None, **kwargs):
+def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, neuron_dropout=None, **kwargs):
     if network_dim is None:
         network_dim = 4  # default
     if network_alpha is None:
@@ -455,7 +455,7 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
         multiplier=multiplier,
         lora_dim=network_dim,
         alpha=network_alpha,
-        dropout=dropout,
+        dropout=neuron_dropout,
         rank_dropout=rank_dropout,
         module_dropout=module_dropout,
         conv_lora_dim=conv_dim,

train_network.py

@@ -212,7 +212,7 @@ def train(args):
     else:
         # LyCORIS will work with this...
         network = network_module.create_network(
-            1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, dropout=args.network_dropout, **net_kwargs
+            1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, neuron_dropout=args.network_dropout, **net_kwargs
         )
     if network is None:
         return
@@ -724,7 +724,7 @@ def train(args):
             progress_bar.set_postfix(**logs)
 
             if args.scale_weight_norms:
-                progress_bar.set_postfix(**max_mean_logs)
+                progress_bar.set_postfix(**{**max_mean_logs, **logs})
 
             if args.logging_dir is not None:
                 logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)