98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
import glob
|
|
import os
|
|
import sys
|
|
import traceback
|
|
|
|
import torch
|
|
|
|
from ldm.util import default
|
|
from modules import devices, shared
|
|
import torch
|
|
from torch import einsum
|
|
from einops import rearrange, repeat
|
|
|
|
|
|
class HypernetworkModule(torch.nn.Module):
|
|
def __init__(self, dim, state_dict):
|
|
super().__init__()
|
|
|
|
self.linear1 = torch.nn.Linear(dim, dim * 2)
|
|
self.linear2 = torch.nn.Linear(dim * 2, dim)
|
|
|
|
self.load_state_dict(state_dict, strict=True)
|
|
self.to(devices.device)
|
|
|
|
def forward(self, x):
|
|
return x + (self.linear2(self.linear1(x)))
|
|
|
|
|
|
class Hypernetwork:
|
|
filename = None
|
|
name = None
|
|
|
|
def __init__(self, filename):
|
|
self.filename = filename
|
|
self.name = os.path.splitext(os.path.basename(filename))[0]
|
|
self.layers = {}
|
|
|
|
state_dict = torch.load(filename, map_location='cpu')
|
|
for size, sd in state_dict.items():
|
|
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
|
|
|
|
|
|
def list_hypernetworks(path):
|
|
res = {}
|
|
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
res[name] = filename
|
|
return res
|
|
|
|
|
|
def load_hypernetwork(filename):
|
|
path = shared.hypernetworks.get(filename, None)
|
|
if path is not None:
|
|
print(f"Loading hypernetwork {filename}")
|
|
try:
|
|
shared.loaded_hypernetwork = Hypernetwork(path)
|
|
except Exception:
|
|
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
else:
|
|
if shared.loaded_hypernetwork is not None:
|
|
print(f"Unloading hypernetwork")
|
|
|
|
shared.loaded_hypernetwork = None
|
|
|
|
|
|
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
|
h = self.heads
|
|
|
|
q = self.to_q(x)
|
|
context = default(context, x)
|
|
|
|
hypernetwork = shared.loaded_hypernetwork
|
|
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
|
|
|
if hypernetwork_layers is not None:
|
|
k = self.to_k(hypernetwork_layers[0](context))
|
|
v = self.to_v(hypernetwork_layers[1](context))
|
|
else:
|
|
k = self.to_k(context)
|
|
v = self.to_v(context)
|
|
|
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
|
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
|
|
|
if mask is not None:
|
|
mask = rearrange(mask, 'b ... -> b (...)')
|
|
max_neg_value = -torch.finfo(sim.dtype).max
|
|
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
|
sim.masked_fill_(~mask, max_neg_value)
|
|
|
|
# attention, what we cannot get enough of
|
|
attn = sim.softmax(dim=-1)
|
|
|
|
out = einsum('b i j, b j d -> b i d', attn, v)
|
|
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
|
return self.to_out(out)
|