Merge branch 'dev' into dev

library/model_util.py

@@ -4,7 +4,7 @@
 import math
 import os
 import torch
-from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging
 from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel
 from safetensors.torch import load_file, save_file
 
@@ -916,7 +916,11 @@ def load_models_from_stable_diffusion_checkpoint(v2, ckpt_path, dtype=None):
     info = text_model.load_state_dict(converted_text_encoder_checkpoint)
   else:
     converted_text_encoder_checkpoint = convert_ldm_clip_checkpoint_v1(state_dict)
+
+    logging.set_verbosity_error()                                                       # don't show annoying warning
     text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
+    logging.set_verbosity_warning()
+
     info = text_model.load_state_dict(converted_text_encoder_checkpoint)
   print("loading text encoder:", info)
 

library/train_util.py

@@ -924,7 +924,9 @@ class FineTuningDataset(BaseDataset):
         elif tags is not None and len(tags) > 0:
           caption = caption + ', ' + tags
           tags_list.append(tags)
-        assert caption is not None and len(caption) > 0, f"caption or tag is required / キャプションまたはタグは必須です:{abs_path}"
+
+        if caption is None:
+          caption = ""
 
         image_info = ImageInfo(image_key, subset.num_repeats, caption, False, abs_path)
         image_info.image_size = img_md.get('train_resolution')
@@ -2207,7 +2209,7 @@ def sample_images(accelerator, args: argparse.Namespace, epoch, steps, device, v
     if epoch is None or epoch % args.sample_every_n_epochs != 0:
       return
   else:
-    if steps % args.sample_every_n_steps != 0:
+    if steps % args.sample_every_n_steps != 0 or epoch is not None:       # steps is not divisible or end of epoch
       return
 
   print(f"generating sample images at step / サンプル画像生成 ステップ: {steps}")
@@ -2353,6 +2355,8 @@ def sample_images(accelerator, args: argparse.Namespace, epoch, steps, device, v
           if negative_prompt is not None:
             negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
 
+        height = max(64, height - height % 8)                 # round to divisible by 8
+        width = max(64, width - width % 8)                 # round to divisible by 8
         print(f"prompt: {prompt}")
         print(f"negative_prompt: {negative_prompt}")
         print(f"height: {height}")