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