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import math
import os
import sys
import traceback
import torch
import numpy as np
from torch import einsum
from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
from modules.hypernetworks import hypernetwork
from modules.shared import opts, device, cmd_opts
from modules import sd_hijack_clip, sd_hijack_open_clip
from modules.sd_hijack_optimizations import invokeAI_mps_available
import ldm.modules.attention
import ldm.modules.diffusionmodules.model
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
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
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
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
if not invokeAI_mps_available and shared.device.type == 'mps':
print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
print("Applying v1 cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
else:
print("Applying cross attention optimization (InvokeAI).")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_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
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():
ldm.modules.attention.BasicTransformerBlock.forward = sd_hijack_checkpoint.BasicTransformerBlock_forward
ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
class StableDiffusionModelHijack:
fixes = None
comments = []
layers = None
circular_enabled = False
clip = None
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
def hijack(self, m):
if 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)
self.clip = m.cond_stage_model
apply_optimizations()
fix_checkpoint()
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) == 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
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 tokenize(self, text):
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
return remade_batch_tokens[0], token_count, sd_hijack_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 = 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:
if devices.has_mps():
attr = attr.to(device="mps", dtype=torch.float32)
else:
attr = attr.to(devices.device)
setattr(self, name, attr)
ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer |