164 lines
6.4 KiB
Python
164 lines
6.4 KiB
Python
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 = []
|
|
|
|
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()
|
|
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 add_conditioning_modifier(self, modifier, ensure_uniqueness=False):
|
|
self.append_model_option('conditioning_modifiers', 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_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
|
|
)
|