99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
import copy
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from ldm_patched.modules.model_patcher import ModelPatcher
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class UnetPatcher(ModelPatcher):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.controlnet_linked_list = None
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self.extra_preserved_memory_during_sampling = 0
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self.extra_model_patchers_during_sampling = []
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def clone(self):
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n = UnetPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device,
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weight_inplace_update=self.weight_inplace_update)
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n.patches = {}
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for k in self.patches:
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n.patches[k] = self.patches[k][:]
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n.object_patches = self.object_patches.copy()
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n.model_options = copy.deepcopy(self.model_options)
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n.model_keys = self.model_keys
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n.controlnet_linked_list = self.controlnet_linked_list
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n.extra_preserved_memory_during_sampling = self.extra_preserved_memory_during_sampling
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n.extra_model_patchers_during_sampling = self.extra_model_patchers_during_sampling.copy()
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return n
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def add_extra_preserved_memory_during_sampling(self, memory_in_bytes: int):
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# Use this to ask Forge to preserve a certain amount of memory during sampling.
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# If GPU VRAM is 8 GB, and memory_in_bytes is 2GB, i.e., memory_in_bytes = 2 * 1024 * 1024 * 1024
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# Then the sampling will always use less than 6GB memory by dynamically offload modules to CPU RAM.
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# You can estimate this using model_management.module_size(any_pytorch_model) to get size of any pytorch models.
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self.extra_preserved_memory_during_sampling += memory_in_bytes
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return
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def add_extra_model_patcher_during_sampling(self, model_patcher: ModelPatcher):
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# Use this to ask Forge to move extra model patchers to GPU during sampling.
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# This method will manage GPU memory perfectly.
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self.extra_model_patchers_during_sampling.append(model_patcher)
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return
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def add_patched_controlnet(self, cnet):
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cnet.set_previous_controlnet(self.controlnet_linked_list)
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self.controlnet_linked_list = cnet
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return
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def list_controlnets(self):
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results = []
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pointer = self.controlnet_linked_list
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while pointer is not None:
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results.append(pointer)
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pointer = pointer.previous_controlnet
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return results
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def append_model_option(self, k, v, ensure_uniqueness=False):
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if k not in self.model_options:
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self.model_options[k] = []
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if ensure_uniqueness and v in self.model_options[k]:
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return
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self.model_options[k].append(v)
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return
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def append_transformer_option(self, k, v, ensure_uniqueness=False):
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if 'transformer_options' not in self.model_options:
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self.model_options['transformer_options'] = {}
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to = self.model_options['transformer_options']
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if k not in to:
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to[k] = []
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if ensure_uniqueness and v in to[k]:
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return
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to[k].append(v)
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return
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def add_conditioning_modifier(self, modifier, ensure_uniqueness=False):
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self.append_model_option('conditioning_modifiers', modifier, ensure_uniqueness)
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return
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def add_block_modifier(self, modifier, ensure_uniqueness=False):
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self.append_transformer_option('block_modifiers', modifier, ensure_uniqueness)
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return
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def add_controlnet_conditioning_modifier(self, modifier, ensure_uniqueness=False):
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self.append_transformer_option('controlnet_conditioning_modifiers', modifier, ensure_uniqueness)
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return
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def set_model_replace_all(self, patch, target="attn1"):
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for block_name in ['input', 'middle', 'output']:
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for number in range(8):
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for transformer_index in range(16):
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self.set_model_patch_replace(patch, target, block_name, number, transformer_index)
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return
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