VictorMylle/Kohya-ss-sd-scripts
1
0
Fork 1
mirror of https://github.com/kohya-ss/sd-scripts.git synced 2026-04-09 06:45:09 +00:00
Code Issues Packages Projects Releases Wiki Activity

update diffusers to 1.16 | train_network

Browse Source
This commit is contained in:
ddPn08
2023-06-01 10:29:48 +09:00
parent f8e8df5a04
commit c8d209d36c
7 changed files with 482 additions and 228 deletions
Download Patch File Download Diff File Expand all files Collapse all files

205
library/train_util.py
View File

@@ -63,6 +63,8 @@ import safetensors.torch
from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline
import library.model_util as model_util
import library.huggingface_util as huggingface_util
from library.attention_processors import FlashAttnProcessor
from library.hypernetwork import replace_attentions_for_hypernetwork
# Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
TOKENIZER_PATH = "openai/clip-vit-large-patch14"
@@ -1630,209 +1632,14 @@ def get_git_revision_hash() -> str:
return "(unknown)"
# flash attention forwards and backwards
# https://arxiv.org/abs/2205.14135
class FlashAttentionFunction(torch.autograd.function.Function):
@staticmethod
@torch.no_grad()
def forward(ctx, q, k, v, mask, causal, q_bucket_size, k_bucket_size):
"""Algorithm 2 in the paper"""
device = q.device
dtype = q.dtype
max_neg_value = -torch.finfo(q.dtype).max
qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
o = torch.zeros_like(q)
all_row_sums = torch.zeros((*q.shape[:-1], 1), dtype=dtype, device=device)
all_row_maxes = torch.full((*q.shape[:-1], 1), max_neg_value, dtype=dtype, device=device)
scale = q.shape[-1] ** -0.5
if not exists(mask):
mask = (None,) * math.ceil(q.shape[-2] / q_bucket_size)
else:
mask = rearrange(mask, "b n -> b 1 1 n")
mask = mask.split(q_bucket_size, dim=-1)
row_splits = zip(
q.split(q_bucket_size, dim=-2),
o.split(q_bucket_size, dim=-2),
mask,
all_row_sums.split(q_bucket_size, dim=-2),
all_row_maxes.split(q_bucket_size, dim=-2),
)
for ind, (qc, oc, row_mask, row_sums, row_maxes) in enumerate(row_splits):
q_start_index = ind * q_bucket_size - qk_len_diff
col_splits = zip(
k.split(k_bucket_size, dim=-2),
v.split(k_bucket_size, dim=-2),
)
for k_ind, (kc, vc) in enumerate(col_splits):
k_start_index = k_ind * k_bucket_size
attn_weights = einsum("... i d, ... j d -> ... i j", qc, kc) * scale
if exists(row_mask):
attn_weights.masked_fill_(~row_mask, max_neg_value)
if causal and q_start_index < (k_start_index + k_bucket_size - 1):
causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu(
q_start_index - k_start_index + 1
)
attn_weights.masked_fill_(causal_mask, max_neg_value)
block_row_maxes = attn_weights.amax(dim=-1, keepdims=True)
attn_weights -= block_row_maxes
exp_weights = torch.exp(attn_weights)
if exists(row_mask):
exp_weights.masked_fill_(~row_mask, 0.0)
block_row_sums = exp_weights.sum(dim=-1, keepdims=True).clamp(min=EPSILON)
new_row_maxes = torch.maximum(block_row_maxes, row_maxes)
exp_values = einsum("... i j, ... j d -> ... i d", exp_weights, vc)
exp_row_max_diff = torch.exp(row_maxes - new_row_maxes)
exp_block_row_max_diff = torch.exp(block_row_maxes - new_row_maxes)
new_row_sums = exp_row_max_diff * row_sums + exp_block_row_max_diff * block_row_sums
oc.mul_((row_sums / new_row_sums) * exp_row_max_diff).add_((exp_block_row_max_diff / new_row_sums) * exp_values)
row_maxes.copy_(new_row_maxes)
row_sums.copy_(new_row_sums)
ctx.args = (causal, scale, mask, q_bucket_size, k_bucket_size)
ctx.save_for_backward(q, k, v, o, all_row_sums, all_row_maxes)
return o
@staticmethod
@torch.no_grad()
def backward(ctx, do):
"""Algorithm 4 in the paper"""
causal, scale, mask, q_bucket_size, k_bucket_size = ctx.args
q, k, v, o, l, m = ctx.saved_tensors
device = q.device
max_neg_value = -torch.finfo(q.dtype).max
qk_len_diff = max(k.shape[-2] - q.shape[-2], 0)
dq = torch.zeros_like(q)
dk = torch.zeros_like(k)
dv = torch.zeros_like(v)
row_splits = zip(
q.split(q_bucket_size, dim=-2),
o.split(q_bucket_size, dim=-2),
do.split(q_bucket_size, dim=-2),
mask,
l.split(q_bucket_size, dim=-2),
m.split(q_bucket_size, dim=-2),
dq.split(q_bucket_size, dim=-2),
)
for ind, (qc, oc, doc, row_mask, lc, mc, dqc) in enumerate(row_splits):
q_start_index = ind * q_bucket_size - qk_len_diff
col_splits = zip(
k.split(k_bucket_size, dim=-2),
v.split(k_bucket_size, dim=-2),
dk.split(k_bucket_size, dim=-2),
dv.split(k_bucket_size, dim=-2),
)
for k_ind, (kc, vc, dkc, dvc) in enumerate(col_splits):
k_start_index = k_ind * k_bucket_size
attn_weights = einsum("... i d, ... j d -> ... i j", qc, kc) * scale
if causal and q_start_index < (k_start_index + k_bucket_size - 1):
causal_mask = torch.ones((qc.shape[-2], kc.shape[-2]), dtype=torch.bool, device=device).triu(
q_start_index - k_start_index + 1
)
attn_weights.masked_fill_(causal_mask, max_neg_value)
exp_attn_weights = torch.exp(attn_weights - mc)
if exists(row_mask):
exp_attn_weights.masked_fill_(~row_mask, 0.0)
p = exp_attn_weights / lc
dv_chunk = einsum("... i j, ... i d -> ... j d", p, doc)
dp = einsum("... i d, ... j d -> ... i j", doc, vc)
D = (doc * oc).sum(dim=-1, keepdims=True)
ds = p * scale * (dp - D)
dq_chunk = einsum("... i j, ... j d -> ... i d", ds, kc)
dk_chunk = einsum("... i j, ... i d -> ... j d", ds, qc)
dqc.add_(dq_chunk)
dkc.add_(dk_chunk)
dvc.add_(dv_chunk)
return dq, dk, dv, None, None, None, None
def replace_unet_modules(unet: diffusers.models.unet_2d_condition.UNet2DConditionModel, mem_eff_attn, xformers):
replace_attentions_for_hypernetwork()
# unet is not used currently, but it is here for future use
if mem_eff_attn:
replace_unet_cross_attn_to_memory_efficient()
unet.set_attn_processor(FlashAttnProcessor())
elif xformers:
replace_unet_cross_attn_to_xformers()
def replace_unet_cross_attn_to_memory_efficient():
print("CrossAttention.forward has been replaced to FlashAttention (not xformers)")
flash_func = FlashAttentionFunction
def forward_flash_attn(self, x, context=None, mask=None):
q_bucket_size = 512
k_bucket_size = 1024
h = self.heads
q = self.to_q(x)
context = context if context is not None else x
context = context.to(x.dtype)
if hasattr(self, "hypernetwork") and self.hypernetwork is not None:
context_k, context_v = self.hypernetwork.forward(x, context)
context_k = context_k.to(x.dtype)
context_v = context_v.to(x.dtype)
else:
context_k = context
context_v = context
k = self.to_k(context_k)
v = self.to_v(context_v)
del context, x
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
out = flash_func.apply(q, k, v, mask, False, q_bucket_size, k_bucket_size)
out = rearrange(out, "b h n d -> b n (h d)")
# diffusers 0.7.0~ わざわざ変えるなよ (;´Д｀)
out = self.to_out[0](out)
out = self.to_out[1](out)
return out
diffusers.models.attention.CrossAttention.forward = forward_flash_attn
unet.enable_xformers_memory_efficient_attention()
def replace_unet_cross_attn_to_xformers():
@@ -3458,10 +3265,10 @@ def sample_images(
unet=unet,
tokenizer=tokenizer,
scheduler=scheduler,
clip_skip=args.clip_skip,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
clip_skip=args.clip_skip,
)
pipeline.to(device)