Initial working commit

__init__.py

@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

nodes.py

@@ -0,0 +1,86 @@
+import os
+import torch
+from .depthfm import DepthFM
+import folder_paths
+import utils
+import model_management
+from contextlib import nullcontext
+
+def convert_dtype(dtype_str):
+    if dtype_str == 'fp32':
+        return torch.float32
+    elif dtype_str == 'fp16':
+        return torch.float16
+    elif dtype_str == 'bf16':
+        return torch.bfloat16
+    else:
+        raise NotImplementedError
+
+class Depth_fm:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "depthfm_model": (folder_paths.get_filename_list("checkpoints"),),
+            "images": ("IMAGE",),
+            "steps": ("INT", {"default": 4}),
+            "ensemble_size": ("INT", {"default": 1}),
+            "dtype": (
+                    [
+                        'fp32',
+                        'fp16',
+                        'bf16',
+                    ], {
+                        "default": 'fp16'
+                    }),
+            "invert": ("BOOLEAN", {"default": True}),
+            "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "process"
+    CATEGORY = "depth_fm"
+
+    def process(self, depthfm_model, images, ensemble_size, steps, dtype, invert, per_batch):
+        device = model_management.get_torch_device()
+        dtype = convert_dtype(dtype)
+
+        custom_config = {
+            "model_path": depthfm_model,
+            "dtype": dtype,
+        }
+        if not hasattr(self, "model") or custom_config != self.current_config:
+            self.current_config = custom_config
+            DEPTHFM_MODEL_PATH = folder_paths.get_full_path("checkpoints", depthfm_model)
+            self.model = DepthFM(DEPTHFM_MODEL_PATH)
+            self.model.eval().to(dtype).to(device)
+
+        images = images.permute(0, 3, 1, 2)
+        images = images * 2.0 - 1.0
+        images = images.to(device)
+
+        pbar = utils.ProgressBar(images.shape[0])
+
+        autocast_condition = not model_management.is_device_mps(device)
+        with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
+            depth_list = []            
+            for start_idx in range(0, images.shape[0], per_batch):
+                sub_images = self.model.predict_depth(images[start_idx:start_idx+per_batch], num_steps=steps, ensemble_size=ensemble_size)
+                depth_list.append(sub_images.cpu())
+                batch_count = sub_images.shape[0]        
+                pbar.update(batch_count)
+        
+        depth = torch.cat(depth_list, dim=0)
+        print(depth.min(), depth.max())
+        depth = depth.repeat(1, 3, 1, 1).permute(0, 2, 3, 1).cpu()
+        if invert:
+            depth = 1.0 - depth
+        return (depth,)
+    
+NODE_CLASS_MAPPINGS = {
+    "Depth_fm": Depth_fm,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Depth_fm": "Depth_fm",
+}