Delete modified code. Since Kijai added support for the giant model, no modifications are needed.

nodes.py

@@ -1,190 +0,0 @@
-
-import torch
-import torch.nn.functional as F
-from torchvision import transforms
-import os
-from contextlib import nullcontext
-
-import comfy.model_management as mm
-from comfy.utils import ProgressBar, load_torch_file
-import folder_paths
-
-from .depth_anything_v2.dpt import DepthAnythingV2
-
-from contextlib import nullcontext
-try:
-    from accelerate import init_empty_weights
-    from accelerate.utils import set_module_tensor_to_device
-    is_accelerate_available = True
-except:
-    pass
-
-class DownloadAndLoadDepthAnythingV2Model:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model": (
-                    [ 
-                        'depth_anything_v2_vits_fp16.safetensors',
-                        'depth_anything_v2_vits_fp32.safetensors',
-                        'depth_anything_v2_vitb_fp16.safetensors',
-                        'depth_anything_v2_vitb_fp32.safetensors',
-                        'depth_anything_v2_vitl_fp16.safetensors',
-                        'depth_anything_v2_vitl_fp32.safetensors',
-                        'depth_anything_v2_vitg_fp32.safetensors',
-                        'depth_anything_v2_metric_hypersim_vitl_fp32.safetensors',
-                        'depth_anything_v2_metric_vkitti_vitl_fp32.safetensors'
-                    ],
-                    {
-                    "default": 'depth_anything_v2_vitl_fp32.safetensors'
-                    }),
-            },
-        }
-
-    RETURN_TYPES = ("DAMODEL",)
-    RETURN_NAMES = ("da_v2_model",)
-    FUNCTION = "loadmodel"
-    CATEGORY = "DepthAnythingV2"
-    DESCRIPTION = """
-Models autodownload to `ComfyUI\models\depthanything` from   
-https://huggingface.co/Kijai/DepthAnythingV2-safetensors/tree/main   
-   
-fp16 reduces quality by a LOT, not recommended.
-"""
-
-    def loadmodel(self, model):
-        device = mm.get_torch_device()
-        dtype = torch.float16 if "fp16" in model else torch.float32
-        model_configs = {
-            'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
-            'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
-            'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
-            'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
-        }
-        custom_config = {
-            'model_name': model,
-        }
-        if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
-            self.current_config = custom_config
-            download_path = os.path.join(folder_paths.models_dir, "depthanything")
-            model_path = os.path.join(download_path, model)
-
-            if not os.path.exists(model_path):
-                print(f"Downloading model to: {model_path}")
-                from huggingface_hub import snapshot_download
-                snapshot_download(repo_id="Kijai/DepthAnythingV2-safetensors", 
-                                  allow_patterns=[f"*{model}*"],
-                                  local_dir=download_path, 
-                                  local_dir_use_symlinks=False)
-
-            print(f"Loading model from: {model_path}")
-
-            if "vitg" in model:
-                encoder = "vitg"
-            elif "vitl" in model:
-                encoder = "vitl"
-            elif "vitb" in model:
-                encoder = "vitb"
-            elif "vits" in model:
-                encoder = "vits"
-
-            if "hypersim" in model:
-                max_depth = 20.0
-            else:
-                max_depth = 80.0
-
-            with (init_empty_weights() if is_accelerate_available else nullcontext()):
-                if 'metric' in model:
-                    self.model = DepthAnythingV2(**{**model_configs[encoder], 'is_metric': True, 'max_depth': max_depth})
-                else:
-                    self.model = DepthAnythingV2(**model_configs[encoder])
-            
-            state_dict = load_torch_file(model_path)
-            if is_accelerate_available:
-                for key in state_dict:
-                    set_module_tensor_to_device(self.model, key, device=device, dtype=dtype, value=state_dict[key])
-            else:
-                self.model.load_state_dict(state_dict)
-
-            self.model.eval()
-            da_model = {
-                "model": self.model,
-                "dtype": dtype,
-                "is_metric": self.model.is_metric
-            }
-           
-        return (da_model,)
-    
-class DepthAnything_V2:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "da_model": ("DAMODEL", ),
-            "images": ("IMAGE", ),
-            },
-        }
-    
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES =("image",)
-    FUNCTION = "process"
-    CATEGORY = "DepthAnythingV2"
-    DESCRIPTION = """
-https://depth-anything-v2.github.io
-"""
-
-    def process(self, da_model, images):
-        device = mm.get_torch_device()
-        offload_device = mm.unet_offload_device()
-        model = da_model['model']
-        dtype=da_model['dtype']
-        
-        B, H, W, C = images.shape
-
-        #images = images.to(device)
-        images = images.permute(0, 3, 1, 2)
-
-        orig_H, orig_W = H, W
-        if W % 14 != 0:
-            W = W - (W % 14)
-        if H % 14 != 0:
-            H = H - (H % 14)
-        if orig_H % 14 != 0 or orig_W % 14 != 0:
-            images = F.interpolate(images, size=(H, W), mode="bilinear")
-        
-        normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        normalized_images = normalize(images)
-        pbar = ProgressBar(B)
-        out = []
-        model.to(device)
-        autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device)
-        with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-            for img in normalized_images:
-                depth = model(img.unsqueeze(0).to(device))
-                depth = (depth - depth.min()) / (depth.max() - depth.min())
-                out.append(depth.cpu())
-                pbar.update(1)
-            model.to(offload_device)
-            depth_out = torch.cat(out, dim=0)
-            depth_out = depth_out.unsqueeze(-1).repeat(1, 1, 1, 3).cpu().float()
-        
-        final_H = (orig_H // 2) * 2
-        final_W = (orig_W // 2) * 2
-
-        
-
-        if depth_out.shape[1] != final_H or depth_out.shape[2] != final_W:
-            depth_out = F.interpolate(depth_out.permute(0, 3, 1, 2), size=(final_H, final_W), mode="bilinear").permute(0, 2, 3, 1)
-        depth_out = (depth_out - depth_out.min()) / (depth_out.max() - depth_out.min())
-        depth_out = torch.clamp(depth_out, 0, 1)
-        if da_model['is_metric']:
-            depth_out = 1 - depth_out
-        return (depth_out,)
-    
-NODE_CLASS_MAPPINGS = {
-    "DepthAnything_V2": DepthAnything_V2,
-    "DownloadAndLoadDepthAnythingV2Model": DownloadAndLoadDepthAnythingV2Model
-}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "DepthAnything_V2": "Depth Anything V2",
-    "DownloadAndLoadDepthAnythingV2Model": "DownloadAndLoadDepthAnythingV2Model"
-}