lora stantard

comfy/lora.py

@@ -12,15 +12,24 @@ LORA_CLIP_MAP = {
 
 
 def load_lora(lora, to_load):
+
     patch_dict = {}
     loaded_keys = set()
     for x in to_load:
+        #import json
+        #with open('/apdcephfs_cq8/share_1367250/xuhuaren/kaiyuan/fork/ComfyUI/result_lora.json', 'w') as fp:
+        #    dict_keys_list = list(lora.keys())
+        #    json.dump(dict_keys_list, fp)
+        #import pdb
+        # dpdb.set_trace() 
+          
+       
+
         alpha_name = "{}.alpha".format(x)
         alpha = None
         if alpha_name in lora.keys():
             alpha = lora[alpha_name].item()
             loaded_keys.add(alpha_name)
-
         dora_scale_name = "{}.dora_scale".format(x)
         dora_scale = None
         if dora_scale_name in lora.keys():
@@ -31,6 +40,7 @@ def load_lora(lora, to_load):
         diffusers_lora = "{}_lora.up.weight".format(x)
         diffusers2_lora = "{}.lora_B.weight".format(x)
         diffusers3_lora = "{}.lora.up.weight".format(x)
+        diffusers4_lora = "{}_lora_B_weight".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
@@ -50,6 +60,10 @@ def load_lora(lora, to_load):
             A_name = diffusers3_lora
             B_name = "{}.lora.down.weight".format(x)
             mid_name = None
+        elif diffusers4_lora in lora.keys():
+            A_name = diffusers4_lora
+            B_name = "{}_lora_A_weight".format(x)
+            mid_name = None
         elif transformers_lora in lora.keys():
             A_name = transformers_lora
             B_name ="{}.lora_linear_layer.down.weight".format(x)
@@ -63,6 +77,8 @@ def load_lora(lora, to_load):
             patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
+            #import pdb
+            #pdb.set_trace()
 
 
         ######## loha

comfy/model_patcher.py

@@ -349,6 +349,7 @@ class ModelPatcher:
         return self.model
 
     def calculate_weight(self, patches, weight, key):
+
         for p in patches:
             strength = p[0]
             v = p[1]
@@ -383,6 +384,7 @@ class ModelPatcher:
                     else:
                         weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
             elif patch_type == "lora": #lora/locon
+
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
                 dora_scale = v[4]
@@ -404,6 +406,8 @@ class ModelPatcher:
                         weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
+
+           
             elif patch_type == "lokr":
                 w1 = v[0]
                 w2 = v[1]
@@ -450,6 +454,7 @@ class ModelPatcher:
                         weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
+
             elif patch_type == "loha":
                 w1a = v[0]
                 w1b = v[1]

custom_nodes/convert_hunyuan_to_comfyui_lora.py

@@ -0,0 +1,16 @@
+
+checkpoint_path2 = "/apdcephfs_cq8/share_1367250/xuhuaren/kaiyuan/fork/ComfyUI/models/loras/adapter_model.safetensors"
+save_path = "/apdcephfs_cq8/share_1367250/xuhuaren/kaiyuan/fork/ComfyUI/models/loras/adapter_model_convert_diffusers.safetensors"
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+
+lora_state_dict = {}
+with safe_open(checkpoint_path2, framework="pt", device=0) as f:
+    for k in f.keys():
+        new_key = "lora_unet_" + "_".join(k[17:].split("."))
+        lora_state_dict[new_key] = f.get_tensor(k) # remove 'basemodel.model'
+
+save_file(lora_state_dict, save_path)
+
+