Merge branch 'dev' into sd3

.github/workflows/typos.yml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: typos-action
-        uses: crate-ci/typos@v1.21.0
+        uses: crate-ci/typos@v1.24.3

README.md

@@ -553,7 +553,12 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
 
 ### Working in progress
 
+- When enlarging images in the script (when the size of the training image is small and bucket_no_upscale is not specified), it has been changed to use Pillow's resize and LANCZOS interpolation instead of OpenCV2's resize and Lanczos4 interpolation. The quality of the image enlargement may be slightly improved. PR [#1426](https://github.com/kohya-ss/sd-scripts/pull/1426) Thanks to sdbds!
+
+- Sample image generation during training now works on non-CUDA devices. PR [#1433](https://github.com/kohya-ss/sd-scripts/pull/1433) Thanks to millie-v!
+
 - `--v_parameterization` is available in `sdxl_train.py`. The results are unpredictable, so use with caution. PR [#1505](https://github.com/kohya-ss/sd-scripts/pull/1505) Thanks to liesened!
+
 - Fused optimizer is available for SDXL training. PR [#1259](https://github.com/kohya-ss/sd-scripts/pull/1259) Thanks to 2kpr!
   - The memory usage during training is significantly reduced by integrating the optimizer's backward pass with step. The training results are the same as before, but if you have plenty of memory, the speed will be slower.
   - Specify the `--fused_backward_pass` option in `sdxl_train.py`. At this time, only Adafactor is supported. Gradient accumulation is not available.

finetune/tag_images_by_wd14_tagger.py

@@ -11,7 +11,7 @@ from PIL import Image
 from tqdm import tqdm
 
 import library.train_util as train_util
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 
 setup_logging()
 import logging
@@ -42,8 +42,10 @@ def preprocess_image(image):
     pad_t = pad_y // 2
     image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
 
-    interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
-    image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
+    if size > IMAGE_SIZE:
+        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
+    else:
+        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
 
     image = image.astype(np.float32)
     return image

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]
 

library/utils.py

@@ -10,6 +10,9 @@ from torchvision import transforms
 from diffusers import EulerAncestralDiscreteScheduler
 import diffusers.schedulers.scheduling_euler_ancestral_discrete
 from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteSchedulerOutput
+import cv2
+from PIL import Image
+import numpy as np
 
 
 def fire_in_thread(f, *args, **kwargs):
@@ -301,6 +304,17 @@ class MemoryEfficientSafeOpen:
             # return byte_tensor.view(torch.uint8).to(torch.float16).reshape(shape)
             raise ValueError(f"Unsupported float8 type: {dtype_str} (upgrade PyTorch to support float8 types)")
 
+def pil_resize(image, size, interpolation=Image.LANCZOS):
+    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # use Pillow resize
+    resized_pil = pil_image.resize(size, interpolation)
+
+    # return cv2 image
+    resized_cv2 = cv2.cvtColor(np.array(resized_pil), cv2.COLOR_RGB2BGR)
+
+    return resized_cv2
+
 
 # TODO make inf_utils.py
 

requirements.txt

@@ -3,7 +3,7 @@ transformers==4.44.0
 diffusers[torch]==0.25.0
 ftfy==6.1.1
 # albumentations==1.3.0
-opencv-python==4.7.0.68
+opencv-python==4.8.1.78
 einops==0.7.0
 pytorch-lightning==1.9.0
 bitsandbytes==0.43.3

tools/detect_face_rotate.py

@@ -15,7 +15,7 @@ import os
 from anime_face_detector import create_detector
 from tqdm import tqdm
 import numpy as np
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -172,7 +172,10 @@ def process(args):
         if scale != 1.0:
           w = int(w * scale + .5)
           h = int(h * scale + .5)
-          face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA if scale < 1.0 else cv2.INTER_LANCZOS4)
+          if scale < 1.0:
+            face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA)
+          else:
+            face_img = pil_resize(face_img, (w, h))
           cx = int(cx * scale + .5)
           cy = int(cy * scale + .5)
           fw = int(fw * scale + .5)

tools/resize_images_to_resolution.py

@@ -6,7 +6,7 @@ import shutil
 import math
 from PIL import Image
 import numpy as np
-from library.utils import setup_logging
+from library.utils import setup_logging, pil_resize
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -24,9 +24,9 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
 
   # Select interpolation method
   if interpolation == 'lanczos4':
-    cv2_interpolation = cv2.INTER_LANCZOS4
+    pil_interpolation = Image.LANCZOS
   elif interpolation == 'cubic':
-    cv2_interpolation = cv2.INTER_CUBIC
+    pil_interpolation = Image.BICUBIC
   else:
     cv2_interpolation = cv2.INTER_AREA
 
@@ -64,7 +64,10 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
         new_width = int(img.shape[1] * math.sqrt(scale_factor))
 
         # Resize image
-        img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+        if cv2_interpolation:
+          img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
+        else:
+          img = pil_resize(img, (new_width, new_height), interpolation=pil_interpolation)
       else:
         new_height, new_width = img.shape[0:2]