(self, p, *args)
| 240 | self.lora_refit_dict = refit_dict |
| 241 | |
| 242 | def process(self, p, *args): |
| 243 | # before unet_init |
| 244 | sd_unet_option = sd_unet.get_unet_option() |
| 245 | if sd_unet_option is None: |
| 246 | return |
| 247 | |
| 248 | if not sd_unet_option.model_name == p.sd_model_name: |
| 249 | gr.Error( |
| 250 | """Selected torch model ({}) does not match the selected TensorRT U-Net ({}). |
| 251 | Please ensure that both models are the same or select Automatic from the SD UNet dropdown.""".format( |
| 252 | p.sd_model_name, sd_unet_option.model_name |
| 253 | ) |
| 254 | ) |
| 255 | self.idx, self.hr_idx = self.get_profile_idx(p, p.sd_model_name, ModelType.UNET) |
| 256 | self.torch_unet = self.idx is None or self.hr_idx is None |
| 257 | |
| 258 | try: |
| 259 | if not self.torch_unet: |
| 260 | self.get_loras(p) |
| 261 | except Exception as e: |
| 262 | gr.Error(e) |
| 263 | raise e |
| 264 | |
| 265 | self.apply_unet(sd_unet_option) |
| 266 | |
| 267 | def apply_unet(self, sd_unet_option): |
| 268 | if ( |
nothing calls this directly
no test coverage detected