[pull] master from comfyanonymous:master

comfy/lora.py

@@ -320,6 +320,7 @@ def model_lora_keys_unet(model, key_map={}):
                 to = diffusers_keys[k]
                 key_lora = k[:-len(".weight")]
                 key_map["diffusion_model.{}".format(key_lora)] = to
+                key_map["transformer.{}".format(key_lora)] = to
                 key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
 
     if isinstance(model, comfy.model_base.Kandinsky5):

comfy/model_patcher.py

@@ -132,14 +132,14 @@ def __init__(self, key, patches, convert_func=None, set_func=None):
     def __call__(self, weight):
         return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
 
-#The above patch logic may cast up the weight to fp32, and do math. Go with fp32 x 3
-LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 3
+LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2
 
 def low_vram_patch_estimate_vram(model, key):
     weight, set_func, convert_func = get_key_weight(model, key)
     if weight is None:
         return 0
-    return weight.numel() * torch.float32.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR
+    model_dtype = getattr(model, "manual_cast_dtype", torch.float32)
+    return weight.numel() * model_dtype.itemsize * LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR
 
 def get_key_weight(model, key):
     set_func = None

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.33.10
-comfyui-workflow-templates==0.7.51
+comfyui-workflow-templates==0.7.54
 comfyui-embedded-docs==0.3.1
 torch
 torchsde