update ver

add install notes

nodes.py

@@ -31,7 +31,7 @@ class DownloadAndLoadSAM2RealtimeModel:
     def INPUT_TYPES(s):
         return {"required": {
             "model": ([ 
-                    'sam2_hiera_tiny.pt',
+                    'sam2_hiera_tiny.pt', 'sam2_hiera_small.pt',
                     ],),
             "segmentor": (
                     ['realtime'],
@@ -70,7 +70,8 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         if not os.path.exists(model_path):
             print(f"Downloading SAM2 model to: {model_path}")
-            url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
+            base_url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/"
+            url = f"{base_url}{model}"
             response = requests.get(url, stream=True)
             response.raise_for_status()
 
@@ -83,8 +84,9 @@ class DownloadAndLoadSAM2RealtimeModel:
 
         config_dir = os.path.join(script_directory, "sam2_configs") 
 
+        model_cfg = model.replace(".pt", ".yaml")
+
         # Code ripped out of sam2.build_sam.build_sam2_camera_predictor to appease Hydra
-        model_cfg = "sam2_hiera_t.yaml" #TODO: remove hardcoded config and path
         with initialize_config_dir(config_dir=config_dir, version_base=None):
             cfg = compose(config_name=model_cfg)
 
@@ -140,12 +142,12 @@ class Sam2RealtimeSegmentation:
             "required": {
                 "images": ("IMAGE",),
                 "sam2_model": ("SAM2MODEL",),
-                "reset_tracking": ("BOOLEAN", {"default": False}),
                 # "keep_model_loaded": ("BOOLEAN", {"default": True}),
             },
             "optional": {
                 "coordinates_positive": ("STRING", ),
                 "coordinates_negative": ("STRING", ),
+                "reset_tracking": ("BOOLEAN", {"default": False}),
                 # "bboxes": ("BBOX", ),
                 # "individual_objects": ("BOOLEAN", {"default": False}),
                 # "mask": ("MASK", ),
@@ -192,9 +194,9 @@ class Sam2RealtimeSegmentation:
         images,
         sam2_model,
         # keep_model_loaded,
-        reset_tracking,
         coordinates_positive=None,
         coordinates_negative=None,
+        reset_tracking=False,
         #point_labels=None,
         # bboxes=None,
         # individual_objects=False,
@@ -250,6 +252,7 @@ class Sam2RealtimeSegmentation:
 
                 # Create colored overlay for processed frames
                 mask_colored = torch.stack([mask] * 3, dim=2)
+
                 overlayed_frame = torch.add(frame * 0.7, mask_colored * 0.3)
                 
                 processed_frames.append(overlayed_frame)

pyproject.toml

@@ -1,6 +1,29 @@
+[project]
+name = "sam2_realtime_forktest"
+description = "This extension provides object segmentation capabilities for ComfyUI workflows"
+version = "0.0.4"
+license = { file = "LICENSE" }
+dependencies = [
+    "sam2_realtime @ git+https://github.com/pschroedl/ComfyUI-SAM2-Realtime.git@main",
+    "pyyaml>6.0.2",
+    "numpy>=1.24.4",
+    "tqdm>=4.66.1",
+    "hydra-core>=1.3.2",
+    "iopath>=0.1.10",
+    "pillow>=9.4.0"
+]
+
+[project.urls]
+Repository = "https://github.com/eliteprox/ComfyUI-SAM2-Realtime"
+
 [build-system]
 requires = [
     "setuptools>=61.0",  
     "torch>=2.3.1",
     ]
 build-backend = "setuptools.build_meta"
+
+[tool.comfy]
+PublisherId = "eliteprox"
+DisplayName = "ComfyUI-SAM2-Realtime-TEST"
+Icon = ""

sam2_configs/sam2_hiera_small.yaml

@@ -0,0 +1,116 @@
+# @package _global_
+
+# Model
+model:
+  _target_: sam2_realtime.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: sam2_realtime.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: sam2_realtime.modeling.backbones.hieradet.Hiera
+      embed_dim: 96
+      num_heads: 1
+      stages: [1, 2, 11, 2]
+      global_att_blocks: [7, 10, 13]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+    neck:
+      _target_: sam2_realtime.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [768, 384, 192, 96]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: sam2_realtime.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: sam2_realtime.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: sam2_realtime.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: sam2_realtime.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: sam2_realtime.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: sam2_realtime.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: sam2_realtime.modeling.memory_encoder.Fuser
+        layer:
+          _target_: sam2_realtime.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 512
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

sam2_configs/sam2_hiera_tiny.yaml

@@ -85,7 +85,7 @@ model:
         num_layers: 2
 
   num_maskmem: 7
-  image_size: 1024
+  image_size: 512
   # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
   # SAM decoder
   sigmoid_scale_for_mem_enc: 20.0

sam2_realtime/build_sam.py

@@ -85,7 +85,7 @@ def build_sam2_camera_predictor(
     apply_postprocessing=True,
 ):
     if GlobalHydra.instance().is_initialized():
-    GlobalHydra.instance().clear()
+        GlobalHydra.instance().clear()
 
     # Initialize Hydra to load the configuration
     config_path = "sam2_configs"

sam2_realtime/sam2_tensor_predictor.py

@@ -14,7 +14,6 @@ from tqdm import tqdm
 from sam2_realtime.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
 from sam2_realtime.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames
 
-
 class SAM2TensorPredictor(SAM2Base):
     """The predictor class to handle user interactions and manage inference states."""
 
@@ -55,7 +54,9 @@ class SAM2TensorPredictor(SAM2Base):
             img = img.float()
         else:
             raise ValueError("Input must be a numpy array or a PyTorch tensor")
-
+        #save original height/width
+        orig_h, orig_w = img.shape[1:]
+        
         # Resize to the target size (supports tensor resizing)
         img = torch.nn.functional.interpolate(
             img.unsqueeze(0), size=(image_size, image_size), mode="bilinear", align_corners=False
@@ -68,28 +69,29 @@ class SAM2TensorPredictor(SAM2Base):
         img /= img_std
 
         height, width = img.shape[1:]  # CHW format
-        return img, width, height
+        return img, width, height, orig_w, orig_h
 
     @torch.inference_mode()
     def load_first_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
-
+            
         self.condition_state = self._init_state(
             offload_video_to_cpu=False, offload_state_to_cpu=False
         )
-        img, width, height = self.prepare_data(img, image_size=self.image_size)
+        img, width, height, orig_w, orig_h = self.prepare_data(img, image_size=self.image_size)
+        self._orig_hw = (orig_w, orig_h)
         self.condition_state["images"] = [img]
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self.condition_state["video_height"] = height
         self.condition_state["video_width"] = width
         self._get_image_feature(frame_idx=0, batch_size=1)
-
+    
     def add_conditioning_frame(self, img):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, width, height = self.prepare_data(img, image_size=self.image_size)
+        img, width, height, _, _ = self.prepare_data(img, image_size=self.image_size)
         self.condition_state["images"].append(img)
         self.condition_state["num_frames"] = len(self.condition_state["images"])
         self._get_image_feature(
@@ -235,14 +237,15 @@ class SAM2TensorPredictor(SAM2Base):
                     points = torch.cat([box_coords, points], dim=1)
                     labels = torch.cat([box_labels, labels], dim=1)
         if normalize_coords:
-            video_H = self.condition_state["video_height"]
-            video_W = self.condition_state["video_width"]
-            points = points / torch.tensor([video_W, video_H]).to(points.device)
+            #video_H = self.condition_state["video_height"]
+            #video_W = self.condition_state["video_width"]
+            orig_w, orig_h = self._orig_hw
+            
+            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
-
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -342,14 +345,16 @@ class SAM2TensorPredictor(SAM2Base):
         if labels.dim() == 1:
             labels = labels.unsqueeze(0)  # add batch dimension
         if normalize_coords:
-            video_H = self.condition_state["video_height"]
-            video_W = self.condition_state["video_width"]
-            points = points / torch.tensor([video_W, video_H]).to(points.device)
+            #video_H = self.condition_state["video_height"]
+            #video_W = self.condition_state["video_width"]
+            orig_w, orig_h = self._orig_hw
+            
+            points = points / torch.tensor([orig_w, orig_h]).to(points.device)
+            
         # scale the (normalized) coordinates by the model's internal image size
         points = points * self.image_size
         points = points.to(self.condition_state["device"])
         labels = labels.to(self.condition_state["device"])
-
         if not clear_old_points:
             point_inputs = point_inputs_per_frame.get(frame_idx, None)
         else:
@@ -769,7 +774,7 @@ class SAM2TensorPredictor(SAM2Base):
         if isinstance(img, torch.Tensor):
             img = img.to(self.device)  # Ensure the tensor is on the correct device
 
-        img, _, _ = self.prepare_data(img, image_size=self.image_size)
+        img, _, _ , _, _ = self.prepare_data(img, image_size=self.image_size)
 
         output_dict = self.condition_state["output_dict"]
         obj_ids = self.condition_state["obj_ids"]