processors multiple fixes

Signed-off-by: Vladimir Mandic <mandic00@live.com>

CHANGELOG.md

@@ -6,6 +6,7 @@
   - `sdnq` warn instead of error for triton
   - `insightface` missing dependencies
   - `pulid` import paths
+  - `processors` init code and multiple fixes
 
 ## Update for 2026-06-16
 

modules/control/proc/anyline/__init__.py

@@ -10,7 +10,7 @@ class AnylineDetector:
     def from_pretrained(cls, pretrained_model_or_path="TheMistoAI/MistoLine", cache_dir=None, local_files_only=False):
         from installer import install
         install('controlnet-aux', quiet=True)
-        from controlnet_aux import AnylineDetector as _AnylineDetector
+        from controlnet_aux.anyline import AnylineDetector as _AnylineDetector
         model = _AnylineDetector.from_pretrained(pretrained_model_or_path, filename="MTEED.pth", subfolder="Anyline", cache_dir=cache_dir)
         return cls(model)
 

modules/control/proc/leres/pix2pix/options/base_options.py

@@ -1,10 +1,8 @@
 import argparse
 import os
-from ..util import util
-# import torch
-from .. import models
-# import pix2pix.data
 import numpy as np
+from modules.control.proc.leres.pix2pix.util import util
+from modules.control.proc.leres.pix2pix import models
 
 class BaseOptions():
     """This class defines options used during both training and test time.

modules/control/proc/lotus/__init__.py

@@ -64,9 +64,13 @@ class LotusDetector:
             # Concatenate along channel dim: [rgb_latents, noise_latents]
             latent_input = torch.cat([rgb_latents, noise_latents], dim=1)
             # UNet forward pass with task embedding as class_labels
+            latent_input = latent_input.to(self.unet.dtype)
+            prompt_embeds = prompt_embeds.to(self.unet.dtype)
+            task_emb = task_emb.to(self.unet.dtype)
             prediction = self.unet(latent_input, timestep, encoder_hidden_states=prompt_embeds, class_labels=task_emb).sample
             # Decode prediction
             prediction = prediction / self.vae.config.scaling_factor
+            prediction = prediction.to(self.vae.dtype)
             decoded = self.vae.decode(prediction).sample
         if opts.control_move_processor:
             self._to("cpu")

modules/control/proc/mediapipe_face.py

@@ -9,12 +9,10 @@ checked_ok = False
 
 def check_dependencies():
     global checked_ok # pylint: disable=global-statement
-    from installer import installed, install
+    from installer import install
     from modules.logger import log
-    packages = [('mediapipe', 'mediapipe')]
+    install('mediapipe')
-    for pkg in packages:
+    # install('protobuf==4.25.6', 'protobuf', no_deps=True, reinstall=True, force=True)
-        if not installed(pkg[1], quiet=True):
-            install(pkg[0], pkg[1], ignore=False)
     try:
         import mediapipe as mp # pylint: disable=unused-import
         checked_ok = True

modules/control/proc/midas/api.py

@@ -2,14 +2,13 @@
 
 import cv2
 import os
-import torch
 import torch.nn as nn
 from torchvision.transforms import Compose
 
-from .midas.dpt_depth import DPTDepthModel
+from modules.control.proc.midas.midas.dpt_depth import DPTDepthModel
-from .midas.midas_net import MidasNet
+from modules.control.proc.midas.midas.midas_net import MidasNet
-from .midas.midas_net_custom import MidasNet_small
+from modules.control.proc.midas.midas.midas_net_custom import MidasNet_small
-from .midas.transforms import Resize, NormalizeImage, PrepareForNet
+from modules.control.proc.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
 from modules.control.util import annotator_ckpts_path
 
 

modules/control/proc/normalbae/__init__.py

@@ -0,0 +1,21 @@
+import numpy as np
+from PIL import Image
+
+
+class NormalBaeDetector:
+    def __init__(self, model):
+        self.model = model
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path="fal/teed", cache_dir=None, local_files_only=False): # pylint: disable=unused-argument
+        from installer import install
+        install('controlnet-aux', quiet=True)
+        from controlnet_aux.normalbae import NormalBaeDetector as _NormalBaeDetector
+        model = _NormalBaeDetector.from_pretrained(pretrained_model_or_path, filename="5_model.pth")
+        return cls(model)
+
+    def __call__(self, image, output_type="pil", **kwargs):
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        result = self.model(image, output_type=output_type)
+        return result

modules/control/proc/segment_anything/utils/onnx.py

@@ -4,14 +4,13 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+from typing import Tuple
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
 
-from typing import Tuple
+from modules.control.proc.segment_anything.modeling import Sam
-
+from modules.control.proc.segment_anything.utils.amg import calculate_stability_score
-from ..modeling import Sam
-from .amg import calculate_stability_score
 
 
 class SamOnnxModel(nn.Module):

modules/control/proc/zoe/zoedepth/models/base_models/midas_repo/hubconf.py

@@ -2,9 +2,9 @@ dependencies = ["torch"]
 
 import torch
 
-from .midas.dpt_depth import DPTDepthModel
+from modules.control.proc.zoe.zoedepth.models.base_models.midas_repo.midas.dpt_depth import DPTDepthModel
-from .midas.midas_net import MidasNet
+from modules.control.proc.zoe.zoedepth.models.base_models.midas_repo.midas.midas_net import MidasNet
-from .midas.midas_net_custom import MidasNet_small
+from modules.control.proc.zoe.zoedepth.models.base_models.midas_repo.midas.midas_net_custom import MidasNet_small
 
 def DPT_BEiT_L_512(pretrained=True, **kwargs):
     """ # This docstring shows up in hub.help()

modules/control/proc/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/next_vit.py

@@ -1,7 +1,6 @@
 import timm
 import torch.nn as nn
 from .utils import activations, forward_default, get_activation
-from ..external.next_vit.classification.nextvit import *
 
 
 def forward_next_vit(pretrained, x):

modules/control/proc/zoe/zoedepth/models/zoedepth/zoedepth_v1.py

@@ -26,12 +26,12 @@ import itertools
 
 import torch
 import torch.nn as nn
-from ..depth_model import DepthModel
+from modules.control.proc.zoe.zoedepth.models.depth_model import DepthModel
-from ...base_models.midas import MidasCore
+from modules.control.proc.zoe.zoedepth.models.base_models.midas import MidasCore
-from ...layers.attractor import AttractorLayer, AttractorLayerUnnormed
+from modules.control.proc.zoe.zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed
-from ...layers.dist_layers import ConditionalLogBinomial
+from modules.control.proc.zoe.zoedepth.models.layers.dist_layers import ConditionalLogBinomial
-from ...layers.localbins_layers import Projector, SeedBinRegressor, SeedBinRegressorUnnormed
+from modules.control.proc.zoe.zoedepth.models.layers.localbins_layers import Projector, SeedBinRegressor, SeedBinRegressorUnnormed
-from ...model_io import load_state_from_resource
+from modules.control.proc.zoe.zoedepth.models.model_io import load_state_from_resource
 
 
 class ZoeDepth(DepthModel):

modules/control/proc/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py

@@ -27,13 +27,13 @@ import itertools
 import torch
 import torch.nn as nn
 
-from ..depth_model import DepthModel
+from modules.control.proc.zoe.zoedepth.models.depth_model import DepthModel
-from ...base_models.midas import MidasCore
+from modules.control.proc.zoe.zoedepth.models.base_models.midas import MidasCore
-from ...layers.attractor import AttractorLayer, AttractorLayerUnnormed
+from modules.control.proc.zoe.zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed
-from ...layers.dist_layers import ConditionalLogBinomial
+from modules.control.proc.zoe.zoedepth.models.layers.dist_layers import ConditionalLogBinomial
-from ...layers.localbins_layers import Projector, SeedBinRegressor, SeedBinRegressorUnnormed
+from modules.control.proc.zoe.zoedepth.models.layers.localbins_layers import Projector, SeedBinRegressor, SeedBinRegressorUnnormed
-from ...layers.patch_transformer import PatchTransformerEncoder
+from modules.control.proc.zoe.zoedepth.models.layers.patch_transformer import PatchTransformerEncoder
-from ...model_io import load_state_from_resource
+from modules.control.proc.zoe.zoedepth.models.model_io import load_state_from_resource
 
 class ZoeDepthNK(DepthModel):
     def __init__(self, core,  bin_conf, bin_centers_type="softplus", bin_embedding_dim=128,

modules/control/processor.py

@@ -18,8 +18,6 @@ processors = [
     # pose
     'OpenPose',
     'DWPose',
-    'MediaPipe Face',
-    'DWPose (ONNX)',
     'RTMW',
     'RTMO',
     'ViTPose',
@@ -31,8 +29,7 @@ processors = [
     'HED',
     'PidiNet',
     'MLSD',
-    'TEED',
+    'Anyline (Legacy)',
-    'Anyline',
     # depth
     'Midas Depth Hybrid',
     'Leres Depth',
@@ -47,16 +44,20 @@ processors = [
     'Marigold Depth LCM',
     'Lotus Depth',
     # normal
-    'Normal Bae',
+    'Normal Bae (Legacy)',
     'DSINE',
     'StableNormal',
     'Marigold Normals',
     # segmentation
-    'SegmentAnything',
+    'SegmentAnything 1.0',
-    'SAM 2.1',
+    'SegmentAnything 2.1',
     'OneFormer',
     # other
     'Shuffle',
+    # legacy
+    'MediaPipe Face (Legacy)',
+    'DWPose (Legacy)',
+    'TEED (Legacy)',
 ]
 
 

modules/control/processors.py

@@ -16,8 +16,6 @@ config = {
     'None': {},
     # pose models
     'OpenPose': {'class': None, 'group': 'Pose', 'checkpoint': True, 'params': {'include_body': True, 'include_hand': False, 'include_face': False}},
-    'MediaPipe Face': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
-    'DWPose (ONNX)': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
     'RTMW': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3, 'draw_body_pose': True, 'draw_hand_pose': True, 'draw_face_pose': True}},
     'RTMO': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
     'ViTPose': {'class': None, 'group': 'Pose', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'usyd-community/vitpose-plus-base'}, 'params': {'min_confidence': 0.3}},
@@ -29,8 +27,6 @@ config = {
     'HED': {'class': None, 'group': 'Edge', 'checkpoint': True, 'params': {'scribble': False, 'safe': False}},
     'PidiNet': {'class': None, 'group': 'Edge', 'checkpoint': True, 'params': {'scribble': False, 'safe': False, 'apply_filter': False}},
     'MLSD': {'class': None, 'group': 'Edge', 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
-    'TEED': {'class': None, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'fal/teed'}, 'params': {}},
-    'Anyline': {'class': None, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'TheMistoAI/MistoLine'}, 'params': {}},
     # depth models
     'Midas Depth Hybrid': {'class': None, 'group': 'Depth', 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}},
     'Leres Depth': {'class': None, 'group': 'Depth', 'checkpoint': True, 'params': {'boost': False, 'thr_a': 0, 'thr_b': 0}},
@@ -45,16 +41,21 @@ config = {
     'Marigold Depth LCM': {'class': None, 'group': 'Depth', 'checkpoint': True, 'params': {'denoising_steps': 1, 'ensemble_size': 1, 'processing_res': 768, 'match_input_res': True, 'color_map': 'None'}, 'load_config': {'pretrained_model_or_path': 'prs-eth/marigold-depth-lcm-v1-0'}},
     'Lotus Depth': {'class': None, 'group': 'Depth', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'jingheya/lotus-depth-g-v2-1-disparity'}, 'params': {'color_map': 'inferno'}},
     # normal models
-    'Normal Bae': {'class': None, 'group': 'Normal', 'checkpoint': True, 'params': {}},
     'DSINE': {'class': None, 'group': 'Normal', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'hugoycj/DSINE-hub'}, 'params': {}},
     'StableNormal': {'class': None, 'group': 'Normal', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'Stable-X/StableNormal'}, 'params': {}},
     'Marigold Normals': {'class': None, 'group': 'Normal', 'checkpoint': True, 'params': {'denoising_steps': 4, 'ensemble_size': 4, 'processing_res': 768, 'match_input_res': True}, 'load_config': {'pretrained_model_or_path': 'prs-eth/marigold-normals-v1-1'}},
     # segmentation models
-    'SegmentAnything': {'class': None, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Base', 'params': {}},
+    'SegmentAnything 1.0': {'class': None, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Base', 'params': {}},
-    'SAM 2.1': {'class': None, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Large', 'load_config': {'pretrained_model_or_path': 'facebook/sam2.1-hiera-large'}, 'params': {}},
+    'SegmentAnything 2.1': {'class': None, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Large', 'load_config': {'pretrained_model_or_path': 'facebook/sam2.1-hiera-large'}, 'params': {}},
     'OneFormer': {'class': None, 'group': 'Segmentation', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'shi-labs/oneformer_ade20k_swin_large'}, 'params': {}},
     # other models
     'Shuffle': {'class': None, 'group': 'Other', 'checkpoint': False, 'params': {}},
+    # legacy models
+    'MediaPipe Face (Legacy)': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
+    'DWPose (Legacy)': {'class': None, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
+    'TEED (Legacy)': {'class': None, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'fal/teed'}, 'params': {}},
+    'Anyline (Legacy)': {'class': None, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'TheMistoAI/MistoLine'}, 'params': {}},
+    'Normal Bae (Legacy)': {'class': None, 'group': 'Normal', 'checkpoint': True, 'params': {}},
 }
 
 
@@ -81,6 +82,7 @@ def delay_load_config():
     from modules.control.proc.depth_pro import DepthProDetector
     from modules.control.proc.depth_anything_v2 import DepthAnythingV2Detector
     from modules.control.proc.teed import TEEDDetector
+    from modules.control.proc.normalbae import NormalBaeDetector
     from modules.control.proc.anyline import AnylineDetector
     from modules.control.proc.rtmlib_pose import RtmlibPoseDetector
     from modules.control.proc.vitpose import ViTPoseDetector
@@ -95,8 +97,8 @@ def delay_load_config():
         'None': {},
         # pose models
         'OpenPose': {'class': OpenposeDetector, 'group': 'Pose', 'checkpoint': True, 'params': {'include_body': True, 'include_hand': False, 'include_face': False}},
-        'MediaPipe Face': {'class': MediapipeFaceDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
+        'MediaPipe Face (Legacy)': {'class': MediapipeFaceDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'max_faces': 1, 'min_confidence': 0.5}},
-        'DWPose (ONNX)': {'class': RtmlibPoseDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
+        'DWPose (Legacy)': {'class': RtmlibPoseDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
         'RTMW': {'class': RtmlibPoseDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3, 'draw_body_pose': True, 'draw_hand_pose': True, 'draw_face_pose': True}},
         'RTMO': {'class': RtmlibPoseDetector, 'group': 'Pose', 'checkpoint': False, 'params': {'min_confidence': 0.3}},
         'ViTPose': {'class': ViTPoseDetector, 'group': 'Pose', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'usyd-community/vitpose-plus-base'}, 'params': {'min_confidence': 0.3}},
@@ -108,8 +110,8 @@ def delay_load_config():
         'HED': {'class': HEDdetector, 'group': 'Edge', 'checkpoint': True, 'params': {'scribble': False, 'safe': False}},
         'PidiNet': {'class': PidiNetDetector, 'group': 'Edge', 'checkpoint': True, 'params': {'scribble': False, 'safe': False, 'apply_filter': False}},
         'MLSD': {'class': MLSDdetector, 'group': 'Edge', 'checkpoint': True, 'params': {'thr_v': 0.1, 'thr_d': 0.1}},
-        'TEED': {'class': TEEDDetector, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'fal/teed'}, 'params': {}},
+        'TEED (Legacy)': {'class': TEEDDetector, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'fal/teed'}, 'params': {}},
-        'Anyline': {'class': AnylineDetector, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'TheMistoAI/MistoLine'}, 'params': {}},
+        'Anyline (Legacy)': {'class': AnylineDetector, 'group': 'Edge', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'TheMistoAI/MistoLine'}, 'params': {}},
         # depth models
         'Midas Depth Hybrid': {'class': MidasDetector, 'group': 'Depth', 'checkpoint': True, 'params': {'bg_th': 0.1, 'depth_and_normal': False}},
         'Leres Depth': {'class': LeresDetector, 'group': 'Depth', 'checkpoint': True, 'params': {'boost': False, 'thr_a': 0, 'thr_b': 0}},
@@ -124,13 +126,13 @@ def delay_load_config():
         'Marigold Depth LCM': {'class': MarigoldDetector, 'group': 'Depth', 'checkpoint': True, 'params': {'denoising_steps': 1, 'ensemble_size': 1, 'processing_res': 768, 'match_input_res': True, 'color_map': 'None'}, 'load_config': {'pretrained_model_or_path': 'prs-eth/marigold-depth-lcm-v1-0'}},
         'Lotus Depth': {'class': LotusDetector, 'group': 'Depth', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'jingheya/lotus-depth-g-v2-1-disparity'}, 'params': {'color_map': 'inferno'}},
         # normal models
-        'Normal Bae': {'class': None, 'group': 'Normal', 'checkpoint': True, 'params': {}},
+        'Normal Bae (Legacy)': {'class': NormalBaeDetector, 'group': 'Normal', 'checkpoint': True, 'params': {}},
         'DSINE': {'class': DSINEDetector, 'group': 'Normal', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'hugoycj/DSINE-hub'}, 'params': {}},
         'StableNormal': {'class': StableNormalDetector, 'group': 'Normal', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'Stable-X/StableNormal'}, 'params': {}},
         'Marigold Normals': {'class': MarigoldNormalsDetector, 'group': 'Normal', 'checkpoint': True, 'params': {'denoising_steps': 4, 'ensemble_size': 4, 'processing_res': 768, 'match_input_res': True}, 'load_config': {'pretrained_model_or_path': 'prs-eth/marigold-normals-v1-1'}},
         # segmentation models
-        'SegmentAnything': {'class': SamDetector, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Base', 'params': {}},
+        'SegmentAnything 1.0': {'class': SamDetector, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Base', 'params': {}},
-        'SAM 2.1': {'class': Sam2Detector, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Large', 'load_config': {'pretrained_model_or_path': 'facebook/sam2.1-hiera-large'}, 'params': {}},
+        'SegmentAnything 2.1': {'class': Sam2Detector, 'group': 'Segmentation', 'checkpoint': True, 'model': 'Large', 'load_config': {'pretrained_model_or_path': 'facebook/sam2.1-hiera-large'}, 'params': {}},
         'OneFormer': {'class': OneFormerDetector, 'group': 'Segmentation', 'checkpoint': True, 'load_config': {'pretrained_model_or_path': 'shi-labs/oneformer_ade20k_swin_large'}, 'params': {}},
         # other models
         'Shuffle': {'class': ContentShuffleDetector, 'group': 'Other', 'checkpoint': False, 'params': {}},

scripts/flux_tools.py

@@ -113,7 +113,7 @@ class FluxToolsScript(scripts_manager.Script):
                 shared.opts.data["sd_model_checkpoint"] = "black-forest-labs/FLUX.1-Canny-dev"
                 sd_models.reload_model_weights(op='model', revision="refs/pr/1")
             if processor_canny is None:
-                from controlnet_aux import CannyDetector
+                from modules.control.proc.canny import CannyDetector
                 processor_canny = CannyDetector()
             if process:
                 control_image = processor_canny(image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024)