Drop basicsr dependency

.github/workflows/run_tests.yaml

@@ -57,7 +57,7 @@ jobs:
           2>&1 | tee output.txt &
       - name: Run tests
         run: |
-          wait-for-it --service 127.0.0.1:7860 -t 600
+          wait-for-it --service 127.0.0.1:7860 -t 20
           python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
       - name: Kill test server
         if: always()

modules/face_restoration_utils.py

@@ -17,6 +17,28 @@
 logger = logging.getLogger(__name__)
 
 
+def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor:
+    """Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor."""
+    assert img.shape[2] == 3, "image must be RGB"
+    if img.dtype == "float64":
+        img = img.astype("float32")
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return torch.from_numpy(img.transpose(2, 0, 1)).float()
+
+
+def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray:
+    """
+    Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range.
+    """
+    tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])
+    assert tensor.dim() == 3, "tensor must be RGB"
+    img_np = tensor.numpy().transpose(1, 2, 0)
+    if img_np.shape[2] == 1:  # gray image, no RGB/BGR required
+        return np.squeeze(img_np, axis=2)
+    return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
+
+
 def create_face_helper(device) -> FaceRestoreHelper:
     from facexlib.detection import retinaface
     from facexlib.utils.face_restoration_helper import FaceRestoreHelper
@@ -36,14 +58,13 @@ def create_face_helper(device) -> FaceRestoreHelper:
 def restore_with_face_helper(
     np_image: np.ndarray,
     face_helper: FaceRestoreHelper,
-    restore_face: Callable[[np.ndarray], np.ndarray],
+    restore_face: Callable[[torch.Tensor], torch.Tensor],
 ) -> np.ndarray:
     """
     Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image.
 
     `restore_face` should take a cropped face image and return a restored face image.
     """
-    from basicsr.utils import img2tensor, tensor2img
     from torchvision.transforms.functional import normalize
     np_image = np_image[:, :, ::-1]
     original_resolution = np_image.shape[0:2]
@@ -56,23 +77,19 @@ def restore_with_face_helper(
         face_helper.align_warp_face()
         logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces))
         for cropped_face in face_helper.cropped_faces:
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+            cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0)
             normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
             cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
 
             try:
                 with torch.no_grad():
-                    restored_face = tensor2img(
-                        restore_face(cropped_face_t),
-                        rgb2bgr=True,
-                        min_max=(-1, 1),
-                    )
+                    cropped_face_t = restore_face(cropped_face_t)
                 devices.torch_gc()
             except Exception:
                 errors.report('Failed face-restoration inference', exc_info=True)
-                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
 
-            restored_face = restored_face.astype('uint8')
+            restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1))
+            restored_face = (restored_face * 255.0).astype('uint8')
             face_helper.add_restored_face(restored_face)
 
         logger.debug("Merging restored faces into image")
@@ -126,7 +143,7 @@ def load_net(self) -> torch.Module:
     def restore_with_helper(
         self,
         np_image: np.ndarray,
-        restore_face: Callable[[np.ndarray], np.ndarray],
+        restore_face: Callable[[torch.Tensor], torch.Tensor],
     ) -> np.ndarray:
         try:
             if self.net is None:

modules/textual_inversion/textual_inversion.py

@@ -11,7 +11,6 @@
 
 import numpy as np
 from PIL import Image, PngImagePlugin
-from torch.utils.tensorboard import SummaryWriter
 
 from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
 import modules.textual_inversion.dataset
@@ -344,6 +343,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         })
 
 def tensorboard_setup(log_directory):
+    from torch.utils.tensorboard import SummaryWriter
     os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
     return SummaryWriter(
             log_dir=os.path.join(log_directory, "tensorboard"),
@@ -448,8 +448,12 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
 
+    tensorboard_writer = None
     if shared.opts.training_enable_tensorboard:
-        tensorboard_writer = tensorboard_setup(log_directory)
+        try:
+            tensorboard_writer = tensorboard_setup(log_directory)
+        except ImportError:
+            errors.report("Error initializing tensorboard", exc_info=True)
 
     pin_memory = shared.opts.pin_memory
 
@@ -622,7 +626,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                         last_saved_image += f", prompt: {preview_text}"
 
-                        if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+                        if tensorboard_writer and shared.opts.training_tensorboard_save_images:
                             tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
 
                     if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

requirements.txt

@@ -2,7 +2,6 @@ GitPython
 Pillow
 accelerate
 
-basicsr
 blendmodes
 clean-fid
 einops

requirements_versions.txt

@@ -1,7 +1,6 @@
 GitPython==3.1.32
 Pillow==9.5.0
 accelerate==0.21.0
-basicsr==1.4.2
 blendmodes==2022
 clean-fid==0.1.35
 einops==0.4.1