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Merge branch 'master' into feat/api-nodes/SD2-realhuman
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Jedrzej Kosinski 2026-04-22 17:48:06 -07:00 committed by GitHub
commit 99aacfaa62
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5 changed files with 16 additions and 11 deletions
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4
comfy/model_base.py
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@ -578,8 +578,8 @@ class Stable_Zero123(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
super().__init__(model_config, model_type, device=device) super().__init__(model_config, model_type, device=device)
self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
self.cc_projection.weight.copy_(cc_projection_weight) self.cc_projection.weight = torch.nn.Parameter(cc_projection_weight.clone())
self.cc_projection.bias.copy_(cc_projection_bias) self.cc_projection.bias = torch.nn.Parameter(cc_projection_bias.clone())
def extra_conds(self, **kwargs): def extra_conds(self, **kwargs):
out = {} out = {}

2
comfy/model_management.py
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@ -1801,7 +1801,7 @@ def debug_memory_summary():
return torch.cuda.memory.memory_summary() return torch.cuda.memory.memory_summary()
return "" return ""
class InterruptProcessingException(Exception): class InterruptProcessingException(BaseException):
pass pass
interrupt_processing_mutex = threading.RLock() interrupt_processing_mutex = threading.RLock()

17
comfy/model_patcher.py
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@ -685,9 +685,9 @@ class ModelPatcher:
sd.pop(k) sd.pop(k)
return sd return sd
def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False): def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False, force_cast=False):
weight, set_func, convert_func = get_key_weight(self.model, key) weight, set_func, convert_func = get_key_weight(self.model, key)
if key not in self.patches: if key not in self.patches and not force_cast:
return weight return weight
inplace_update = self.weight_inplace_update or inplace_update inplace_update = self.weight_inplace_update or inplace_update
@ -695,7 +695,7 @@ class ModelPatcher:
if key not in self.backup and not return_weight: if key not in self.backup and not return_weight:
self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
temp_dtype = comfy.model_management.lora_compute_dtype(device_to) temp_dtype = comfy.model_management.lora_compute_dtype(device_to) if key in self.patches else None
if device_to is not None: if device_to is not None:
temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True) temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
else: else:
@ -703,9 +703,10 @@ class ModelPatcher:
if convert_func is not None: if convert_func is not None:
temp_weight = convert_func(temp_weight, inplace=True) temp_weight = convert_func(temp_weight, inplace=True)
out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) if key in self.patches else temp_weight
if set_func is None: if set_func is None:
out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key)) if key in self.patches:
out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key))
if return_weight: if return_weight:
return out_weight return out_weight
elif inplace_update: elif inplace_update:
@ -1584,7 +1585,7 @@ class ModelPatcherDynamic(ModelPatcher):
key = key_param_name_to_key(n, param_key) key = key_param_name_to_key(n, param_key)
if key in self.backup: if key in self.backup:
comfy.utils.set_attr_param(self.model, key, self.backup[key].weight) comfy.utils.set_attr_param(self.model, key, self.backup[key].weight)
self.patch_weight_to_device(key, device_to=device_to) self.patch_weight_to_device(key, device_to=device_to, force_cast=True)
weight, _, _ = get_key_weight(self.model, key) weight, _, _ = get_key_weight(self.model, key)
if weight is not None: if weight is not None:
self.model.model_loaded_weight_memory += weight.numel() * weight.element_size() self.model.model_loaded_weight_memory += weight.numel() * weight.element_size()
@ -1609,6 +1610,10 @@ class ModelPatcherDynamic(ModelPatcher):
m._v = vbar.alloc(v_weight_size) m._v = vbar.alloc(v_weight_size)
allocated_size += v_weight_size allocated_size += v_weight_size
for param in params:
if param not in ("weight", "bias"):
force_load_param(self, param, device_to)
else: else:
for param in params: for param in params:
key = key_param_name_to_key(n, param) key = key_param_name_to_key(n, param)

2
manager_requirements.txt
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@ -1 +1 @@
comfyui_manager==4.1 comfyui_manager==4.2.1

2
utils/install_util.py
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@ -39,7 +39,7 @@ def get_required_packages_versions():
if len(s) == 2: if len(s) == 2:
version_str = s[-1] version_str = s[-1]
if not is_valid_version(version_str): if not is_valid_version(version_str):
logging.error(f"Invalid version format in requirements.txt: {version_str}") logging.debug(f"Invalid version format for {s[0]} in requirements.txt: {version_str}")
continue continue
out[s[0]] = version_str out[s[0]] = version_str
return out.copy() return out.copy()