import torch import einops import ldm.modules.encoders.modules import ldm.modules.attention_compat from transformers import logging from ldm.modules.attention_compat import default def disable_verbosity(): logging.set_verbosity_error() print('logging improved.') return def enable_sliced_attention(): ldm.modules.attention_compat.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 print('Enabled clip hacks.') 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 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)