418 lines
15 KiB
Python
418 lines
15 KiB
Python
import math
|
|
import os
|
|
import sys
|
|
import traceback
|
|
import torch
|
|
import numpy as np
|
|
from torch import einsum
|
|
|
|
from modules.shared import opts, device, cmd_opts
|
|
|
|
from ldm.util import default
|
|
from einops import rearrange
|
|
import ldm.modules.attention
|
|
import ldm.modules.diffusionmodules.model
|
|
|
|
|
|
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
|
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
k = self.to_k(context)
|
|
v = self.to_v(context)
|
|
del context, x
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
|
|
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
|
for i in range(0, q.shape[0], 2):
|
|
end = i + 2
|
|
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
|
|
s1 *= self.scale
|
|
|
|
s2 = s1.softmax(dim=-1)
|
|
del s1
|
|
|
|
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
|
del s2
|
|
|
|
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
|
del r1
|
|
|
|
return self.to_out(r2)
|
|
|
|
|
|
# taken from https://github.com/Doggettx/stable-diffusion
|
|
def split_cross_attention_forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q_in = self.to_q(x)
|
|
context = default(context, x)
|
|
k_in = self.to_k(context) * self.scale
|
|
v_in = self.to_v(context)
|
|
del context, x
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
|
del q_in, k_in, v_in
|
|
|
|
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
|
|
|
stats = torch.cuda.memory_stats(q.device)
|
|
mem_active = stats['active_bytes.all.current']
|
|
mem_reserved = stats['reserved_bytes.all.current']
|
|
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
|
mem_free_torch = mem_reserved - mem_active
|
|
mem_free_total = mem_free_cuda + mem_free_torch
|
|
|
|
gb = 1024 ** 3
|
|
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
|
modifier = 3 if q.element_size() == 2 else 2.5
|
|
mem_required = tensor_size * modifier
|
|
steps = 1
|
|
|
|
if mem_required > mem_free_total:
|
|
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
|
|
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
|
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
|
|
|
if steps > 64:
|
|
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
|
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
|
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
|
|
|
|
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
|
for i in range(0, q.shape[1], slice_size):
|
|
end = i + slice_size
|
|
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
|
|
|
|
s2 = s1.softmax(dim=-1, dtype=q.dtype)
|
|
del s1
|
|
|
|
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
|
del s2
|
|
|
|
del q, k, v
|
|
|
|
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
|
del r1
|
|
|
|
return self.to_out(r2)
|
|
|
|
def nonlinearity_hijack(x):
|
|
# swish
|
|
t = torch.sigmoid(x)
|
|
x *= t
|
|
del t
|
|
|
|
return x
|
|
|
|
def cross_attention_attnblock_forward(self, x):
|
|
h_ = x
|
|
h_ = self.norm(h_)
|
|
q1 = self.q(h_)
|
|
k1 = self.k(h_)
|
|
v = self.v(h_)
|
|
|
|
# compute attention
|
|
b, c, h, w = q1.shape
|
|
|
|
q2 = q1.reshape(b, c, h*w)
|
|
del q1
|
|
|
|
q = q2.permute(0, 2, 1) # b,hw,c
|
|
del q2
|
|
|
|
k = k1.reshape(b, c, h*w) # b,c,hw
|
|
del k1
|
|
|
|
h_ = torch.zeros_like(k, device=q.device)
|
|
|
|
stats = torch.cuda.memory_stats(q.device)
|
|
mem_active = stats['active_bytes.all.current']
|
|
mem_reserved = stats['reserved_bytes.all.current']
|
|
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
|
mem_free_torch = mem_reserved - mem_active
|
|
mem_free_total = mem_free_cuda + mem_free_torch
|
|
|
|
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
|
mem_required = tensor_size * 2.5
|
|
steps = 1
|
|
|
|
if mem_required > mem_free_total:
|
|
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
|
|
|
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
|
for i in range(0, q.shape[1], slice_size):
|
|
end = i + slice_size
|
|
|
|
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
|
w2 = w1 * (int(c)**(-0.5))
|
|
del w1
|
|
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
|
|
del w2
|
|
|
|
# attend to values
|
|
v1 = v.reshape(b, c, h*w)
|
|
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
|
|
del w3
|
|
|
|
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
|
|
del v1, w4
|
|
|
|
h2 = h_.reshape(b, c, h, w)
|
|
del h_
|
|
|
|
h3 = self.proj_out(h2)
|
|
del h2
|
|
|
|
h3 += x
|
|
|
|
return h3
|
|
|
|
class StableDiffusionModelHijack:
|
|
ids_lookup = {}
|
|
word_embeddings = {}
|
|
word_embeddings_checksums = {}
|
|
fixes = None
|
|
comments = []
|
|
dir_mtime = None
|
|
layers = None
|
|
circular_enabled = False
|
|
|
|
def load_textual_inversion_embeddings(self, dirname, model):
|
|
mt = os.path.getmtime(dirname)
|
|
if self.dir_mtime is not None and mt <= self.dir_mtime:
|
|
return
|
|
|
|
self.dir_mtime = mt
|
|
self.ids_lookup.clear()
|
|
self.word_embeddings.clear()
|
|
|
|
tokenizer = model.cond_stage_model.tokenizer
|
|
|
|
def const_hash(a):
|
|
r = 0
|
|
for v in a:
|
|
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
|
|
return r
|
|
|
|
def process_file(path, filename):
|
|
name = os.path.splitext(filename)[0]
|
|
|
|
data = torch.load(path)
|
|
|
|
# textual inversion embeddings
|
|
if 'string_to_param' in data:
|
|
param_dict = data['string_to_param']
|
|
if hasattr(param_dict, '_parameters'):
|
|
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
|
|
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
|
emb = next(iter(param_dict.items()))[1]
|
|
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
|
|
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
|
|
|
|
emb = next(iter(data.values()))
|
|
if len(emb.shape) == 1:
|
|
emb = emb.unsqueeze(0)
|
|
|
|
self.word_embeddings[name] = emb.detach()
|
|
self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1))&0xffff:04x}'
|
|
|
|
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
|
|
|
|
first_id = ids[0]
|
|
if first_id not in self.ids_lookup:
|
|
self.ids_lookup[first_id] = []
|
|
self.ids_lookup[first_id].append((ids, name))
|
|
|
|
for fn in os.listdir(dirname):
|
|
try:
|
|
process_file(os.path.join(dirname, fn), fn)
|
|
except Exception:
|
|
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
continue
|
|
|
|
print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
|
|
|
|
def hijack(self, m):
|
|
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
|
|
|
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
|
|
m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
|
|
|
if cmd_opts.opt_split_attention_v1:
|
|
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
|
|
elif not cmd_opts.disable_opt_split_attention:
|
|
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
|
|
ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
|
|
ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
|
|
|
|
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 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'
|
|
|
|
|
|
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|
def __init__(self, wrapped, hijack):
|
|
super().__init__()
|
|
self.wrapped = wrapped
|
|
self.hijack = hijack
|
|
self.tokenizer = wrapped.tokenizer
|
|
self.max_length = wrapped.max_length
|
|
self.token_mults = {}
|
|
|
|
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
|
for text, ident in tokens_with_parens:
|
|
mult = 1.0
|
|
for c in text:
|
|
if c == '[':
|
|
mult /= 1.1
|
|
if c == ']':
|
|
mult *= 1.1
|
|
if c == '(':
|
|
mult *= 1.1
|
|
if c == ')':
|
|
mult /= 1.1
|
|
|
|
if mult != 1.0:
|
|
self.token_mults[ident] = mult
|
|
|
|
def forward(self, text):
|
|
self.hijack.fixes = []
|
|
self.hijack.comments = []
|
|
remade_batch_tokens = []
|
|
id_start = self.wrapped.tokenizer.bos_token_id
|
|
id_end = self.wrapped.tokenizer.eos_token_id
|
|
maxlen = self.wrapped.max_length - 2
|
|
used_custom_terms = []
|
|
|
|
cache = {}
|
|
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
|
|
batch_multipliers = []
|
|
for tokens in batch_tokens:
|
|
tuple_tokens = tuple(tokens)
|
|
|
|
if tuple_tokens in cache:
|
|
remade_tokens, fixes, multipliers = cache[tuple_tokens]
|
|
else:
|
|
fixes = []
|
|
remade_tokens = []
|
|
multipliers = []
|
|
mult = 1.0
|
|
|
|
i = 0
|
|
while i < len(tokens):
|
|
token = tokens[i]
|
|
|
|
possible_matches = self.hijack.ids_lookup.get(token, None)
|
|
|
|
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
|
|
if mult_change is not None:
|
|
mult *= mult_change
|
|
elif possible_matches is None:
|
|
remade_tokens.append(token)
|
|
multipliers.append(mult)
|
|
else:
|
|
found = False
|
|
for ids, word in possible_matches:
|
|
if tokens[i:i+len(ids)] == ids:
|
|
emb_len = int(self.hijack.word_embeddings[word].shape[0])
|
|
fixes.append((len(remade_tokens), word))
|
|
remade_tokens += [0] * emb_len
|
|
multipliers += [mult] * emb_len
|
|
i += len(ids) - 1
|
|
found = True
|
|
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
|
|
break
|
|
|
|
if not found:
|
|
remade_tokens.append(token)
|
|
multipliers.append(mult)
|
|
|
|
i += 1
|
|
|
|
if len(remade_tokens) > maxlen - 2:
|
|
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
|
ovf = remade_tokens[maxlen - 2:]
|
|
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
|
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
|
|
|
self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
|
|
|
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
|
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
|
|
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
|
|
|
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
|
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
|
|
|
remade_batch_tokens.append(remade_tokens)
|
|
self.hijack.fixes.append(fixes)
|
|
batch_multipliers.append(multipliers)
|
|
|
|
if len(used_custom_terms) > 0:
|
|
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
|
|
|
tokens = torch.asarray(remade_batch_tokens).to(device)
|
|
outputs = self.wrapped.transformer(input_ids=tokens)
|
|
z = outputs.last_hidden_state
|
|
|
|
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
|
batch_multipliers = torch.asarray(batch_multipliers).to(device)
|
|
original_mean = z.mean()
|
|
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
|
new_mean = z.mean()
|
|
z *= original_mean / new_mean
|
|
|
|
return z
|
|
|
|
|
|
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 not None:
|
|
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
|
for offset, word in fixes:
|
|
emb = self.embeddings.word_embeddings[word]
|
|
emb_len = min(tensor.shape[0]-offset, emb.shape[0])
|
|
tensor[offset:offset+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
|
|
|
|
return inputs_embeds
|
|
|
|
|
|
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()
|