add: LoRA Encoder

diffsynth/pipelines/flux_image.py

@@ -106,38 +106,6 @@ class FluxImagePipeline(BasePipeline):
     def enable_lora_magic(self):
         pass
 
-    def load_lora(self, model, lora_config, alpha=1, hotload=False):
-        if isinstance(lora_config, str):
-            path = lora_config
-        else:
-            lora_config.download_if_necessary()
-            path = lora_config.path
-            
-        state_dict = load_state_dict(path, torch_dtype=self.torch_dtype, device="cpu")
-        loader = self.lora_loader(torch_dtype=self.torch_dtype, device=self.device)
-        state_dict = loader.convert_state_dict(state_dict)
-        loaded_count = 0
-        for key in tqdm(state_dict, desc="Applying LoRA"):
-            if ".lora_A." in key:
-                layer_name = key.split(".lora_A.")[0]
-                module = model
-                try:
-                    parts = layer_name.split(".")
-                    for part in parts:
-                        if part.isdigit():
-                            module = module[int(part)]
-                        else:
-                            module = getattr(module, part)
-                except AttributeError:
-                    continue
-                
-                w_a = state_dict[key].to(device=module.weight.device, dtype=module.weight.dtype)
-                w_b_key = key.replace("lora_A", "lora_B")
-                if w_b_key not in state_dict: continue
-                w_b = state_dict[w_b_key].to(device=module.weight.device, dtype=module.weight.dtype)
-                delta_w = torch.mm(w_b, w_a)
-                module.weight.data += delta_w * alpha
-                loaded_count += 1
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,