Update web-demos/hugging_face/inpainter/base_inpainter.py

web-demos/hugging_face/inpainter/base_inpainter.py

@@ -197,30 +197,31 @@ class ProInpainter:
 		"""
 		
 		frames = []
-		for i in range(len(npframes)):
-			frames.append(Image.fromarray(npframes[i].astype('uint8'), mode="RGB"))
-		del npframes
-
-		size = frames[0].size
-		# The ouput size should be divided by 2 so that it can encoded by libx264
-		size = (int(ratio*size[0])//2*2, int(ratio*size[1])//2*2)
-
-		# set propainter size limit to 720 to reduce memory usage
-		if max(size[0], size[1]) > 720:
-			scale = 720.0 / max(size[0], size[1])
-			# The ouput size should be divided by 2 so that it can encoded by libx264
-			size = (int(scale*size[0])//2*2, int(scale*size[1])//2*2)
-
-		frames_len = len(frames)
-		frames, size, out_size = resize_frames(frames, size)
-		flow_masks, masks_dilated = read_mask_demo(masks, frames_len, size, dilate_radius, dilate_radius)
-		w, h = size
-
-		frames_inp = [np.array(f).astype(np.uint8) for f in frames]
-		frames = to_tensors()(frames).unsqueeze(0) * 2 - 1    
-		flow_masks = to_tensors()(flow_masks).unsqueeze(0)
-		masks_dilated = to_tensors()(masks_dilated).unsqueeze(0)
-		frames, flow_masks, masks_dilated = frames.to(self.device), flow_masks.to(self.device), masks_dilated.to(self.device)
+        for i in range(len(npframes)):
+            frames.append(Image.fromarray(npframes[i].astype('uint8'), mode="RGB"))
+        del npframes
+
+        size = frames[0].size
+# Применяем ratio, если не 1.0
+        if ratio != 1.0:
+            size = (int(ratio * size[0]) // 2 * 2, int(ratio * size[1]) // 2 * 2)
+        else:
+            size = (size[0] // 2 * 2, size[1] // 2 * 2)  # просто округление до чётного
+
+# ⚠️ УБРАНО авто-уменьшение до 720p
+# frames останутся оригинального (или заданного ratio) размера
+
+        frames_len = len(frames)
+        frames, size, out_size = resize_frames(frames, size)
+        flow_masks, masks_dilated = read_mask_demo(masks, frames_len, size, dilate_radius, dilate_radius)
+        w, h = size
+
+        frames_inp = [np.array(f).astype(np.uint8) for f in frames]
+        frames = to_tensors()(frames).unsqueeze(0) * 2 - 1    
+        flow_masks = to_tensors()(flow_masks).unsqueeze(0)
+        masks_dilated = to_tensors()(masks_dilated).unsqueeze(0)
+        frames, flow_masks, masks_dilated = frames.to(self.device), flow_masks.to(self.device), masks_dilated.to(self.device)
+
 		
 		##############################################
 		# ProPainter inference