277 lines
12 KiB
Python
277 lines
12 KiB
Python
import torch
|
|
from torch.nn.functional import silu
|
|
from types import MethodType
|
|
|
|
import modules.textual_inversion.textual_inversion
|
|
from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
|
|
from modules.hypernetworks import hypernetwork
|
|
from modules.shared import cmd_opts
|
|
from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
|
|
|
|
import ldm.modules.attention
|
|
import ldm.modules.diffusionmodules.model
|
|
import ldm.modules.diffusionmodules.openaimodel
|
|
import ldm.models.diffusion.ddim
|
|
import ldm.models.diffusion.plms
|
|
import ldm.modules.encoders.modules
|
|
|
|
attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
|
|
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
|
|
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
|
|
|
|
# new memory efficient cross attention blocks do not support hypernets and we already
|
|
# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
|
|
ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
|
|
ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
|
|
|
|
# silence new console spam from SD2
|
|
ldm.modules.attention.print = lambda *args: None
|
|
ldm.modules.diffusionmodules.model.print = lambda *args: None
|
|
|
|
|
|
def apply_optimizations():
|
|
undo_optimizations()
|
|
|
|
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
|
ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
|
|
|
|
optimization_method = None
|
|
|
|
can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
|
|
|
|
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
|
|
print("Applying xformers cross attention optimization.")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
|
|
optimization_method = 'xformers'
|
|
elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
|
|
print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
|
|
optimization_method = 'sdp-no-mem'
|
|
elif cmd_opts.opt_sdp_attention and can_use_sdp:
|
|
print("Applying scaled dot product cross attention optimization.")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
|
|
optimization_method = 'sdp'
|
|
elif cmd_opts.opt_sub_quad_attention:
|
|
print("Applying sub-quadratic cross attention optimization.")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
|
|
optimization_method = 'sub-quadratic'
|
|
elif cmd_opts.opt_split_attention_v1:
|
|
print("Applying v1 cross attention optimization.")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
|
optimization_method = 'V1'
|
|
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):
|
|
print("Applying cross attention optimization (InvokeAI).")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
|
|
optimization_method = 'InvokeAI'
|
|
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
|
print("Applying cross attention optimization (Doggettx).")
|
|
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
|
optimization_method = 'Doggettx'
|
|
|
|
return optimization_method
|
|
|
|
|
|
def undo_optimizations():
|
|
ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
|
|
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
|
|
|
|
|
|
def fix_checkpoint():
|
|
"""checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
|
|
checkpoints to be added when not training (there's a warning)"""
|
|
|
|
pass
|
|
|
|
|
|
def weighted_loss(sd_model, pred, target, mean=True):
|
|
#Calculate the weight normally, but ignore the mean
|
|
loss = sd_model._old_get_loss(pred, target, mean=False)
|
|
|
|
#Check if we have weights available
|
|
weight = getattr(sd_model, '_custom_loss_weight', None)
|
|
if weight is not None:
|
|
loss *= weight
|
|
|
|
#Return the loss, as mean if specified
|
|
return loss.mean() if mean else loss
|
|
|
|
def weighted_forward(sd_model, x, c, w, *args, **kwargs):
|
|
try:
|
|
#Temporarily append weights to a place accessible during loss calc
|
|
sd_model._custom_loss_weight = w
|
|
|
|
#Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
|
|
#Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
|
|
if not hasattr(sd_model, '_old_get_loss'):
|
|
sd_model._old_get_loss = sd_model.get_loss
|
|
sd_model.get_loss = MethodType(weighted_loss, sd_model)
|
|
|
|
#Run the standard forward function, but with the patched 'get_loss'
|
|
return sd_model.forward(x, c, *args, **kwargs)
|
|
finally:
|
|
try:
|
|
#Delete temporary weights if appended
|
|
del sd_model._custom_loss_weight
|
|
except AttributeError as e:
|
|
pass
|
|
|
|
#If we have an old loss function, reset the loss function to the original one
|
|
if hasattr(sd_model, '_old_get_loss'):
|
|
sd_model.get_loss = sd_model._old_get_loss
|
|
del sd_model._old_get_loss
|
|
|
|
def apply_weighted_forward(sd_model):
|
|
#Add new function 'weighted_forward' that can be called to calc weighted loss
|
|
sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
|
|
|
|
def undo_weighted_forward(sd_model):
|
|
try:
|
|
del sd_model.weighted_forward
|
|
except AttributeError as e:
|
|
pass
|
|
|
|
|
|
class StableDiffusionModelHijack:
|
|
fixes = None
|
|
comments = []
|
|
layers = None
|
|
circular_enabled = False
|
|
clip = None
|
|
optimization_method = None
|
|
|
|
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
|
|
|
|
def __init__(self):
|
|
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
|
|
|
|
def hijack(self, m):
|
|
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
|
|
model_embeddings = m.cond_stage_model.roberta.embeddings
|
|
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
|
|
m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
|
|
|
|
elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
|
|
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
|
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
|
|
m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
|
|
|
elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
|
|
m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
|
|
m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
|
|
|
apply_weighted_forward(m)
|
|
if m.cond_stage_key == "edit":
|
|
sd_hijack_unet.hijack_ddpm_edit()
|
|
|
|
self.optimization_method = apply_optimizations()
|
|
|
|
self.clip = m.cond_stage_model
|
|
|
|
def flatten(el):
|
|
flattened = [flatten(children) for children in el.children()]
|
|
res = [el]
|
|
for c in flattened:
|
|
res += c
|
|
return res
|
|
|
|
self.layers = flatten(m)
|
|
|
|
def undo_hijack(self, m):
|
|
if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
|
|
m.cond_stage_model = m.cond_stage_model.wrapped
|
|
|
|
elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
|
|
m.cond_stage_model = m.cond_stage_model.wrapped
|
|
|
|
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
|
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
|
|
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
|
|
elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
|
|
m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
|
|
m.cond_stage_model = m.cond_stage_model.wrapped
|
|
|
|
undo_optimizations()
|
|
undo_weighted_forward(m)
|
|
|
|
self.apply_circular(False)
|
|
self.layers = None
|
|
self.clip = None
|
|
|
|
def apply_circular(self, enable):
|
|
if self.circular_enabled == enable:
|
|
return
|
|
|
|
self.circular_enabled = enable
|
|
|
|
for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
|
|
layer.padding_mode = 'circular' if enable else 'zeros'
|
|
|
|
def clear_comments(self):
|
|
self.comments = []
|
|
|
|
def get_prompt_lengths(self, text):
|
|
_, token_count = self.clip.process_texts([text])
|
|
|
|
return token_count, self.clip.get_target_prompt_token_count(token_count)
|
|
|
|
|
|
class EmbeddingsWithFixes(torch.nn.Module):
|
|
def __init__(self, wrapped, embeddings):
|
|
super().__init__()
|
|
self.wrapped = wrapped
|
|
self.embeddings = embeddings
|
|
|
|
def forward(self, input_ids):
|
|
batch_fixes = self.embeddings.fixes
|
|
self.embeddings.fixes = None
|
|
|
|
inputs_embeds = self.wrapped(input_ids)
|
|
|
|
if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
|
|
return inputs_embeds
|
|
|
|
vecs = []
|
|
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
|
for offset, embedding in fixes:
|
|
emb = devices.cond_cast_unet(embedding.vec)
|
|
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
|
|
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
|
|
|
|
vecs.append(tensor)
|
|
|
|
return torch.stack(vecs)
|
|
|
|
|
|
def add_circular_option_to_conv_2d():
|
|
conv2d_constructor = torch.nn.Conv2d.__init__
|
|
|
|
def conv2d_constructor_circular(self, *args, **kwargs):
|
|
return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
|
|
|
|
torch.nn.Conv2d.__init__ = conv2d_constructor_circular
|
|
|
|
|
|
model_hijack = StableDiffusionModelHijack()
|
|
|
|
|
|
def register_buffer(self, name, attr):
|
|
"""
|
|
Fix register buffer bug for Mac OS.
|
|
"""
|
|
|
|
if type(attr) == torch.Tensor:
|
|
if attr.device != devices.device:
|
|
attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
|
|
|
|
setattr(self, name, attr)
|
|
|
|
|
|
ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
|
|
ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
|