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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:
is_accelerate_available = False
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_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 "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"
} |