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Initial commit CORE-135

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Talmaj Marinc
2026-05-11 20:45:07 +02:00
parent 462c27fdb2
commit 137a05b93d
10 changed files with 1702 additions and 1 deletions
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84
comfy/model_detection.py
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@@ -849,6 +849,90 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
return dit_config
# Depth Anything 3 (Apache-2.0 monocular variants: Small/Base/Mono-Large/Metric-Large).
if '{}backbone.pretrained.patch_embed.proj.weight'.format(key_prefix) in state_dict_keys:
dit_config = {}
dit_config["image_model"] = "DepthAnything3"
patch_w = state_dict['{}backbone.pretrained.patch_embed.proj.weight'.format(key_prefix)]
embed_dim = patch_w.shape[0]
depth = count_blocks(state_dict_keys, '{}backbone.pretrained.blocks.'.format(key_prefix) + '{}.')
# Backbone preset is determined by embed_dim (matches vits/vitb/vitl/vitg).
backbone_name = {384: "vits", 768: "vitb", 1024: "vitl", 1536: "vitg"}.get(embed_dim)
if backbone_name is None:
return None
dit_config["backbone_name"] = backbone_name
# Detect DA3 extensions on top of vanilla DINOv2.
has_camera_token = '{}backbone.pretrained.camera_token'.format(key_prefix) in state_dict_keys
# qk-norm shows up as `attn.q_norm.weight` on enabled blocks.
qknorm_indices = [
i for i in range(depth)
if '{}backbone.pretrained.blocks.{}.attn.q_norm.weight'.format(key_prefix, i) in state_dict_keys
]
qknorm_start = qknorm_indices[0] if qknorm_indices else -1
# The DA3 main-series configs always set alt_start == qknorm_start == rope_start.
# cat_token=True is implied by the presence of camera_token.
if has_camera_token:
dit_config["alt_start"] = qknorm_start
dit_config["rope_start"] = qknorm_start
dit_config["qknorm_start"] = qknorm_start
dit_config["cat_token"] = True
else:
dit_config["alt_start"] = -1
dit_config["rope_start"] = -1
dit_config["qknorm_start"] = -1
dit_config["cat_token"] = False
# Detect head type and config.
has_aux = '{}head.scratch.refinenet1_aux.out_conv.weight'.format(key_prefix) in state_dict_keys
if has_aux:
dit_config["head_type"] = "dualdpt"
# DualDPT: dim_in = 2 * embed_dim (because cat_token doubles token width).
head_dim_in = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1]
out_channels = [
state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0]
for i in range(4)
]
features = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0]
dit_config["head_dim_in"] = head_dim_in
dit_config["head_output_dim"] = 2
dit_config["head_features"] = features
dit_config["head_out_channels"] = out_channels
dit_config["head_use_sky_head"] = False
else:
dit_config["head_type"] = "dpt"
head_dim_in = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1]
out_channels = [
state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0]
for i in range(4)
]
features = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0]
output_dim = state_dict[
'{}head.scratch.output_conv2.2.weight'.format(key_prefix)
].shape[0]
dit_config["head_dim_in"] = head_dim_in
dit_config["head_output_dim"] = output_dim
dit_config["head_features"] = features
dit_config["head_out_channels"] = out_channels
dit_config["head_use_sky_head"] = (
'{}head.scratch.sky_output_conv2.0.weight'.format(key_prefix) in state_dict_keys
)
# out_layers: hard-coded per upstream YAML config (depth-aware default).
if depth >= 24:
# vitl: depths used vary between DA3-Large (DualDPT) and Mono/Metric (DPT).
if has_aux:
dit_config["out_layers"] = [11, 15, 19, 23]
else:
dit_config["out_layers"] = [4, 11, 17, 23]
else:
# vits/vitb: 12 blocks
dit_config["out_layers"] = [5, 7, 9, 11]
return dit_config
if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image
dit_config = {}
dit_config["image_model"] = "ernie"