Merge branch 'dev' into sd3

library/train_util.py

@@ -73,7 +73,7 @@ import library.model_util as model_util
 import library.huggingface_util as huggingface_util
 import library.sai_model_spec as sai_model_spec
 import library.deepspeed_utils as deepspeed_utils
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 
 setup_logging()
 import logging
@@ -1708,7 +1708,7 @@ class DreamBoothDataset(BaseDataset):
         def load_dreambooth_dir(subset: DreamBoothSubset):
             if not os.path.isdir(subset.image_dir):
                 logger.warning(f"not directory: {subset.image_dir}")
-                return [], []
+                return [], [], []
 
             info_cache_file = os.path.join(subset.image_dir, self.IMAGE_INFO_CACHE_FILE)
             use_cached_info_for_subset = subset.cache_info
@@ -2263,9 +2263,7 @@ class ControlNetDataset(BaseDataset):
                 # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}"
                 # resize to target
                 if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]:
-                    cond_img = cv2.resize(
-                        cond_img, (int(target_size_hw[1]), int(target_size_hw[0])), interpolation=cv2.INTER_LANCZOS4
-                    )
+                    cond_img = pil_resize(cond_img, (int(target_size_hw[1]), int(target_size_hw[0])))
 
             if flipped:
                 cond_img = cond_img[:, ::-1, :].copy()  # copy to avoid negative stride
@@ -2659,7 +2657,10 @@ def trim_and_resize_if_required(
 
     if image_width != resized_size[0] or image_height != resized_size[1]:
         # リサイズする
-        image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        if image_width > resized_size[0] and image_height > resized_size[1]:
+            image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        else:
+            image = pil_resize(image, resized_size)
 
     image_height, image_width = image.shape[0:2]
 
@@ -5657,7 +5658,7 @@ def sample_images_common(
     clean_memory_on_device(accelerator.device)
 
     torch.set_rng_state(rng_state)
-    if cuda_rng_state is not None:
+    if torch.cuda.is_available() and cuda_rng_state is not None:
         torch.cuda.set_rng_state(cuda_rng_state)
     vae.to(org_vae_device)
 
@@ -5691,11 +5692,13 @@ def sample_image_inference(
 
     if seed is not None:
         torch.manual_seed(seed)
-        torch.cuda.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed(seed)
     else:
         # True random sample image generation
         torch.seed()
-        torch.cuda.seed()
+        if torch.cuda.is_available():
+            torch.cuda.seed()
 
     scheduler = get_my_scheduler(
         sample_sampler=sampler_name,
@@ -5730,8 +5733,9 @@ def sample_image_inference(
             controlnet_image=controlnet_image,
         )
 
-    with torch.cuda.device(torch.cuda.current_device()):
-        torch.cuda.empty_cache()
+    if torch.cuda.is_available():
+        with torch.cuda.device(torch.cuda.current_device()):
+            torch.cuda.empty_cache()
 
     image = pipeline.latents_to_image(latents)[0]