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Create separate loader nodes for DepthAnything and ZoeDepth #315

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73 changes: 56 additions & 17 deletions node_wrappers/depth_anything.py
Original file line number Diff line number Diff line change
@@ -1,51 +1,90 @@
from ..utils import common_annotator_call, create_node_input_types
from ..utils import common_annotator_call, create_node_input_types, MAX_RESOLUTION
import comfy.model_management as model_management
import folder_paths

class Depth_Anything_Loader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"], {"default": "depth_anything_vitl14.pth"}) }}
RETURN_TYPES = ("DEPTH_MODEL",)
FUNCTION = "load_checkpoint"

CATEGORY = "ControlNet Preprocessors/Depth Loader"

def load_checkpoint(self, ckpt_name):
from controlnet_aux.depth_anything import DepthAnythingDetector
model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
return (model, )


class Depth_Anything_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return create_node_input_types(
ckpt_name=(["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"], {"default": "depth_anything_vitl14.pth"})
)
return {
"required": {
"image": ("IMAGE",),
"model": ("DEPTH_MODEL",)
},
"optional": {
"resolution": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64})
}
}

RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"

CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"

def execute(self, image, ckpt_name, resolution=512, **kwargs):
from controlnet_aux.depth_anything import DepthAnythingDetector

model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
def execute(self, image, model, resolution=512, **kwargs):
out = common_annotator_call(model, image, resolution=resolution)
del model
return (out, )

class Zoe_Depth_Anything_Loader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "environment": (["indoor", "outdoor"], {"default": "indoor"})}}
RETURN_TYPES = ("ZOEDEPTH_MODEL",)
FUNCTION = "load_checkpoint"

CATEGORY = "ControlNet Preprocessors/Depth Loader"

def load_checkpoint(self, environment):
from controlnet_aux.zoe import ZoeDepthAnythingDetector
ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt"
model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
return (model, )

class Zoe_Depth_Anything_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return create_node_input_types(
environment=(["indoor", "outdoor"], {"default": "indoor"})
)
return {
"required": {
"image": ("IMAGE",),
"model": ("ZOEDEPTH_MODEL",)
},
"optional": {
"resolution": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64})
}
}

RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"

CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"

def execute(self, image, environment, resolution=512, **kwargs):
from controlnet_aux.zoe import ZoeDepthAnythingDetector
ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt"
model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
def execute(self, image, model, resolution=512, **kwargs):
out = common_annotator_call(model, image, resolution=resolution)
del model
return (out, )

NODE_CLASS_MAPPINGS = {
"DepthAnythingLoader": Depth_Anything_Loader,
"DepthAnythingPreprocessor": Depth_Anything_Preprocessor,
"Zoe_DepthAnythingLoader": Zoe_Depth_Anything_Loader,
"Zoe_DepthAnythingPreprocessor": Zoe_Depth_Anything_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DepthAnythingLoader": "Depth Anything Loader",
"DepthAnythingPreprocessor": "Depth Anything",
"Zoe_DepthAnythingLoader": "Zoe Depth Anything Loader",
"Zoe_DepthAnythingPreprocessor": "Zoe Depth Anything"
}