Remove useless dependencies, Florence2 fixes

nodes.py

@@ -128,31 +128,32 @@ class Florence2toCoordinates:
     CATEGORY = "SAM2"
 
     def segment(self, data, index, batch=False):
-        print(data)
         try:
             coordinates = coordinates.replace("'", '"')
             coordinates = json.loads(coordinates)
         except:
             coordinates = data
-        print("Type of data:", type(data))
-        print("Data:", data)
+
         if len(data)==0:
             return (json.dumps([{'x': 0, 'y': 0}]),)
         center_points = []
 
+        def get_bboxes(item):
+            return item["bboxes"] if isinstance(item, dict) else item
+
         if index.strip():  # Check if index is not empty
             indexes = [int(i) for i in index.split(",")]
         else:  # If index is empty, use all indices from data[0]
-            indexes = list(range(len(data[0])))
+            indexes = list(range(len(get_bboxes(data[0]))))
 
         print("Indexes:", indexes)
         bboxes = []
         
         if batch:
             for idx in indexes:
-                if 0 <= idx < len(data[0]):
+                if 0 <= idx < len(get_bboxes(data[0])):
                     for i in range(len(data)):
-                        bbox = data[i][idx] if i < len(data) else data[0].get("bboxes", [])[idx]
+                        bbox = get_bboxes(data[i])[idx]
                         min_x, min_y, max_x, max_y = bbox
                         center_x = int((min_x + max_x) / 2)
                         center_y = int((min_y + max_y) / 2)
@@ -160,8 +161,8 @@ class Florence2toCoordinates:
                         bboxes.append(bbox)
         else:
             for idx in indexes:
-                if 0 <= idx < len(data[0]):
-                    bbox = data[0].get("bboxes", [])[idx] if isinstance(data[0], dict) else data[0][idx]
+                if 0 <= idx < len(get_bboxes(data[0])):
+                    bbox = get_bboxes(data[0])[idx]
                     min_x, min_y, max_x, max_y = bbox
                     center_x = int((min_x + max_x) / 2)
                     center_y = int((min_y + max_y) / 2)

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "comfyui-segment-anything-2"
 description = "Nodes to use [a/segment-anything-2](https://github.com/facebookresearch/segment-anything-2) for image or video segmentation."
-version = "1.0.0"
+version = "1.0.1"
 license = {file = "LICENSE"}
-dependencies = ["pyyaml", "numpy<=1.26.4"]
+dependencies = []
 
 [project.urls]
 Repository = "https://github.com/kijai/ComfyUI-segment-anything-2"

requirements.txt

@@ -1,2 +0,0 @@
-pyyaml
-iopath

sam2/modeling/backbones/hieradet.py

@@ -10,7 +10,7 @@ from typing import List, Tuple, Union
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from iopath.common.file_io import g_pathmgr
+#from iopath.common.file_io import g_pathmgr
 
 from ....sam2.modeling.backbones.utils import (
     PatchEmbed,
@@ -264,10 +264,10 @@ class Hiera(nn.Module):
             else [self.blocks[-1].dim_out]
         )
 
-        if weights_path is not None:
-            with g_pathmgr.open(weights_path, "rb") as f:
-                chkpt = torch.load(f, map_location="cpu")
-            logging.info("loading Hiera", self.load_state_dict(chkpt, strict=False))
+        # if weights_path is not None:
+        #     with g_pathmgr.open(weights_path, "rb") as f:
+        #         chkpt = torch.load(f, map_location="cpu")
+        #     logging.info("loading Hiera", self.load_state_dict(chkpt, strict=False))
 
     def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:
         h, w = hw

sam2/utils/misc.py

@@ -19,12 +19,12 @@ def get_sdpa_settings():
         old_gpu = torch.cuda.get_device_properties(0).major < 7
         # only use Flash Attention on Ampere (8.0) or newer GPUs
         use_flash_attn = torch.cuda.get_device_properties(0).major >= 8 and platform.system() == 'Linux'
-        if not use_flash_attn:
-            warnings.warn(
-                "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.",
-                category=UserWarning,
-                stacklevel=2,
-            )
+        # if not use_flash_attn:
+        #     warnings.warn(
+        #         "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.",
+        #         category=UserWarning,
+        #         stacklevel=2,
+        #     )
         # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only
         # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases)
         pytorch_version = tuple(int(v) for v in torch.__version__.split(".")[:2])