remove sam2

app.py

@@ -6,8 +6,9 @@ from torchvision import transforms
 from transformers import AutoModelForImageSegmentation
 from diffusers import FluxFillPipeline
 from PIL import Image, ImageOps
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-import numpy as np
+
+# from sam2.sam2_image_predictor import SAM2ImagePredictor
+# import numpy as np
 from simple_lama_inpainting import SimpleLama
 from contextlib import contextmanager
 
@@ -134,35 +135,36 @@ def rmbg(image=None, url=None):
 
 
 def mask_generation(image=None, d=None):
-    # use bfloat16 for the entire notebook
-    # torch.autocast("cuda", dtype=torch.bfloat16).__enter__()
-    # # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
-    # if torch.cuda.get_device_properties(0).major >= 8:
-    #     torch.backends.cuda.matmul.allow_tf32 = True
-    #     torch.backends.cudnn.allow_tf32 = True
-    d = eval(d)  # convert this to dictionary
-    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-        predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-large")
-        predictor.set_image(image)
-        input_point = np.array(d["input_points"])
-        input_label = np.array(d["input_labels"])
-        masks, scores, logits = predictor.predict(
-            point_coords=input_point,
-            point_labels=input_label,
-            multimask_output=True,
-        )
-    sorted_ind = np.argsort(scores)[::-1]
-    masks = masks[sorted_ind]
-    scores = scores[sorted_ind]
-    logits = logits[sorted_ind]
-
-    out = []
-    for i in range(len(masks)):
-        m = Image.fromarray(masks[i] * 255).convert("L")
-        comp = Image.composite(image, m, m)
-        out.append((comp, f"image {i}"))
-
-    return out
+    return image
+    # # use bfloat16 for the entire notebook
+    # # torch.autocast("cuda", dtype=torch.bfloat16).__enter__()
+    # # # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
+    # # if torch.cuda.get_device_properties(0).major >= 8:
+    # #     torch.backends.cuda.matmul.allow_tf32 = True
+    # #     torch.backends.cudnn.allow_tf32 = True
+    # d = eval(d)  # convert this to dictionary
+    # with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
+    #     predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-large")
+    #     predictor.set_image(image)
+    #     input_point = np.array(d["input_points"])
+    #     input_label = np.array(d["input_labels"])
+    #     masks, scores, logits = predictor.predict(
+    #         point_coords=input_point,
+    #         point_labels=input_label,
+    #         multimask_output=True,
+    #     )
+    # sorted_ind = np.argsort(scores)[::-1]
+    # masks = masks[sorted_ind]
+    # scores = scores[sorted_ind]
+    # logits = logits[sorted_ind]
+
+    # out = []
+    # for i in range(len(masks)):
+    #     m = Image.fromarray(masks[i] * 255).convert("L")
+    #     comp = Image.composite(image, m, m)
+    #     out.append((comp, f"image {i}"))
+
+    # return out
 
 
 def erase(image=None, mask=None):