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import copy
import torch
from ldm_patched.modules.model_patcher import ModelPatcher
from ldm_patched.modules.sample import convert_cond
from ldm_patched.modules.samplers import encode_model_conds
class UnetPatcher(ModelPatcher):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.controlnet_linked_list = None
self.extra_preserved_memory_during_sampling = 0
self.extra_model_patchers_during_sampling = []
self.extra_concat_condition = None
def clone(self):
n = UnetPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device,
weight_inplace_update=self.weight_inplace_update)
n.patches = {}
for k in self.patches:
n.patches[k] = self.patches[k][:]
n.object_patches = self.object_patches.copy()
n.model_options = copy.deepcopy(self.model_options)
n.model_keys = self.model_keys
n.controlnet_linked_list = self.controlnet_linked_list
n.extra_preserved_memory_during_sampling = self.extra_preserved_memory_during_sampling
n.extra_model_patchers_during_sampling = self.extra_model_patchers_during_sampling.copy()
n.extra_concat_condition = self.extra_concat_condition
return n
def add_extra_preserved_memory_during_sampling(self, memory_in_bytes: int):
# Use this to ask Forge to preserve a certain amount of memory during sampling.
# If GPU VRAM is 8 GB, and memory_in_bytes is 2GB, i.e., memory_in_bytes = 2 * 1024 * 1024 * 1024
# Then the sampling will always use less than 6GB memory by dynamically offload modules to CPU RAM.
# You can estimate this using model_management.module_size(any_pytorch_model) to get size of any pytorch models.
self.extra_preserved_memory_during_sampling += memory_in_bytes
return
def add_extra_model_patcher_during_sampling(self, model_patcher: ModelPatcher):
# Use this to ask Forge to move extra model patchers to GPU during sampling.
# This method will manage GPU memory perfectly.
self.extra_model_patchers_during_sampling.append(model_patcher)
return
def add_extra_torch_module_during_sampling(self, m: torch.nn.Module, cast_to_unet_dtype: bool = True):
# Use this method to bind an extra torch.nn.Module to this UNet during sampling.
# This model `m` will be delegated to Forge memory management system.
# `m` will be loaded to GPU everytime when sampling starts.
# `m` will be unloaded if necessary.
# `m` will influence Forge's judgement about use GPU memory or
# capacity and decide whether to use module offload to make user's batch size larger.
# Use cast_to_unet_dtype if you want `m` to have same dtype with unet during sampling.
if cast_to_unet_dtype:
m.to(self.model.diffusion_model.dtype)
patcher = ModelPatcher(model=m, load_device=self.load_device, offload_device=self.offload_device)
self.add_extra_model_patcher_during_sampling(patcher)
return patcher
def add_patched_controlnet(self, cnet):
cnet.set_previous_controlnet(self.controlnet_linked_list)
self.controlnet_linked_list = cnet
return
def list_controlnets(self):
results = []
pointer = self.controlnet_linked_list
while pointer is not None:
results.append(pointer)
pointer = pointer.previous_controlnet
return results
def append_model_option(self, k, v, ensure_uniqueness=False):
if k not in self.model_options:
self.model_options[k] = []
if ensure_uniqueness and v in self.model_options[k]:
return
self.model_options[k].append(v)
return
def append_transformer_option(self, k, v, ensure_uniqueness=False):
if 'transformer_options' not in self.model_options:
self.model_options['transformer_options'] = {}
to = self.model_options['transformer_options']
if k not in to:
to[k] = []
if ensure_uniqueness and v in to[k]:
return
to[k].append(v)
return
def set_transformer_option(self, k, v):
if 'transformer_options' not in self.model_options:
self.model_options['transformer_options'] = {}
self.model_options['transformer_options'][k] = v
return
def add_conditioning_modifier(self, modifier, ensure_uniqueness=False):
self.append_model_option('conditioning_modifiers', modifier, ensure_uniqueness)
return
def add_sampler_pre_cfg_function(self, modifier, ensure_uniqueness=False):
self.append_model_option('sampler_pre_cfg_function', modifier, ensure_uniqueness)
return
def set_memory_peak_estimation_modifier(self, modifier):
self.model_options['memory_peak_estimation_modifier'] = modifier
return
def add_alphas_cumprod_modifier(self, modifier, ensure_uniqueness=False):
"""
For some reasons, this function only works in A1111's Script.process_batch(self, p, *args, **kwargs)
For example, below is a worked modification:
class ExampleScript(scripts.Script):
def process_batch(self, p, *args, **kwargs):
unet = p.sd_model.forge_objects.unet.clone()
def modifier(x):
return x ** 0.5
unet.add_alphas_cumprod_modifier(modifier)
p.sd_model.forge_objects.unet = unet
return
This add_alphas_cumprod_modifier is the only patch option that should be used in process_batch()
All other patch options should be called in process_before_every_sampling()
"""
self.append_model_option('alphas_cumprod_modifiers', modifier, ensure_uniqueness)
return
def add_block_modifier(self, modifier, ensure_uniqueness=False):
self.append_transformer_option('block_modifiers', modifier, ensure_uniqueness)
return
def add_block_inner_modifier(self, modifier, ensure_uniqueness=False):
self.append_transformer_option('block_inner_modifiers', modifier, ensure_uniqueness)
return
def add_controlnet_conditioning_modifier(self, modifier, ensure_uniqueness=False):
self.append_transformer_option('controlnet_conditioning_modifiers', modifier, ensure_uniqueness)
return
def set_controlnet_model_function_wrapper(self, wrapper):
self.set_transformer_option('controlnet_model_function_wrapper', wrapper)
return
def set_model_replace_all(self, patch, target="attn1"):
for block_name in ['input', 'middle', 'output']:
for number in range(16):
for transformer_index in range(16):
self.set_model_patch_replace(patch, target, block_name, number, transformer_index)
return
def encode_conds_after_clip(self, conds, noise, prompt_type="positive"):
return encode_model_conds(
model_function=self.model.extra_conds,
conds=convert_cond(conds),
noise=noise,
device=noise.device,
prompt_type=prompt_type
)
def load_frozen_patcher(self, state_dict, strength):
patch_dict = {}
for k, w in state_dict.items():
model_key, patch_type, weight_index = k.split('::')
if model_key not in patch_dict:
patch_dict[model_key] = {}
if patch_type not in patch_dict[model_key]:
patch_dict[model_key][patch_type] = [None] * 16
patch_dict[model_key][patch_type][int(weight_index)] = w
patch_flat = {}
for model_key, v in patch_dict.items():
for patch_type, weight_list in v.items():
patch_flat[model_key] = (patch_type, weight_list)
self.add_patches(patches=patch_flat, strength_patch=float(strength), strength_model=1.0)
return
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