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- import torch
- import einops
- from transformers import logging
- from . import ldm
- from .ldm.modules.attention import default
- def disable_verbosity():
- logging.set_verbosity_error()
- return
- def enable_sliced_attention():
- ldm.modules.attention.CrossAttention.forward = _hacked_sliced_attentin_forward
- print('Enabled sliced_attention.')
- return
- def hack_everything(clip_skip=0):
- disable_verbosity()
- ldm.modules.encoders.modules.FrozenCLIPEmbedder.forward = _hacked_clip_forward
- ldm.modules.encoders.modules.FrozenCLIPEmbedder.clip_skip = clip_skip
- return
- # Written by Lvmin
- def _hacked_clip_forward(self, text):
- PAD = self.tokenizer.pad_token_id
- EOS = self.tokenizer.eos_token_id
- BOS = self.tokenizer.bos_token_id
- text = [t.replace('_', ' ') for t in text]
- def tokenize(t):
- return self.tokenizer(t, truncation=False, add_special_tokens=False)["input_ids"]
- def transformer_encode(t):
- if self.clip_skip > 1:
- rt = self.transformer(input_ids=t, output_hidden_states=True)
- return self.transformer.text_model.final_layer_norm(rt.hidden_states[-self.clip_skip])
- else:
- return self.transformer(input_ids=t, output_hidden_states=False).last_hidden_state
- def split(x):
- return x[75 * 0: 75 * 1], x[75 * 1: 75 * 2], x[75 * 2: 75 * 3]
- def pad(x, p, i):
- return x[:i] if len(x) >= i else x + [p] * (i - len(x))
- raw_tokens_list = tokenize(text)
- tokens_list = []
- for raw_tokens in raw_tokens_list:
- raw_tokens_123 = split(raw_tokens)
- raw_tokens_123 = [[BOS] + raw_tokens_i + [EOS] for raw_tokens_i in raw_tokens_123]
- raw_tokens_123 = [pad(raw_tokens_i, PAD, 77) for raw_tokens_i in raw_tokens_123]
- tokens_list.append(raw_tokens_123)
- tokens_list = torch.IntTensor(tokens_list).to(self.device)
- feed = einops.rearrange(tokens_list, 'b f i -> (b f) i')
- y = transformer_encode(feed)
- z = einops.rearrange(y, '(b f) i c -> b (f i) c', f=3)
- return z
- # Stolen from https://github.com/basujindal/stable-diffusion/blob/main/optimizedSD/splitAttention.py
- def _hacked_sliced_attentin_forward(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: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
- limit = k.shape[0]
- att_step = 1
- q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
- k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
- v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
- q_chunks.reverse()
- k_chunks.reverse()
- v_chunks.reverse()
- sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
- del k, q, v
- for i in range(0, limit, att_step):
- q_buffer = q_chunks.pop()
- k_buffer = k_chunks.pop()
- v_buffer = v_chunks.pop()
- sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
- del k_buffer, q_buffer
- # attention, what we cannot get enough of, by chunks
- sim_buffer = sim_buffer.softmax(dim=-1)
- sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
- del v_buffer
- sim[i:i + att_step, :, :] = sim_buffer
- del sim_buffer
- sim = einops.rearrange(sim, '(b h) n d -> b n (h d)', h=h)
- return self.to_out(sim)
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