support inpaint models from fooocus

put inpaint_v26.fooocus.patch in models\ControlNet, control SDXL models only
To get same algorithm as Fooocus, set "Stop at" (Ending Control Step) to 0.5
Fooocus always use 0.5 but in Forge users may use other values.
Results are best when stop at < 0.7. The model is not optimized with ending timesteps > 0.7
Supports inpaint_global_harmonious, inpaint_only, inpaint_only+lama.
In theory the inpaint_only+lama always outperform Fooocus in object removal task (but not all tasks).

ldm_patched/modules/model_management.py

@@ -5,7 +5,6 @@ from ldm_patched.modules.args_parser import args
 import ldm_patched.modules.utils
 import torch
 import sys
-import os
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -59,7 +58,7 @@ try:
 except:
     pass
 
-if args.always_cpu or os.environ.get("FORGE_CQ_TEST", ""):
+if args.always_cpu:
     cpu_state = CPUState.CPU
 
 def is_intel_xpu():

ldm_patched/modules/model_patcher.py

@@ -5,6 +5,10 @@ import inspect
 import ldm_patched.modules.utils
 import ldm_patched.modules.model_management
 
+
+extra_weight_calculators = {}
+
+
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
         self.size = size
@@ -329,6 +333,8 @@ class ModelPatcher:
                 b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
 
                 weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
+            elif patch_type in extra_weight_calculators:
+                weight = extra_weight_calculators[patch_type](weight, alpha, v)
             else:
                 print("patch type not recognized", patch_type, key)