(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs)
| 1829 | self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha) |
| 1830 | |
| 1831 | def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: |
| 1832 | self_cls_name = self.__class__.__name__ |
| 1833 | deprecate( |
| 1834 | self_cls_name, |
| 1835 | "0.26.0", |
| 1836 | ( |
| 1837 | f"Make sure use {self_cls_name[4:]} instead by setting" |
| 1838 | "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using" |
| 1839 | " `LoraLoaderMixin.load_lora_weights`" |
| 1840 | ), |
| 1841 | ) |
| 1842 | attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device) |
| 1843 | attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device) |
| 1844 | attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device) |
| 1845 | attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device) |
| 1846 | |
| 1847 | attn._modules.pop("processor") |
| 1848 | attn.processor = AttnProcessor2_0() |
| 1849 | return attn.processor(attn, hidden_states, *args, **kwargs) |
| 1850 | |
| 1851 | |
| 1852 | class LoRAXFormersAttnProcessor(nn.Module): |
nothing calls this directly
no test coverage detected