multimodalart HF staff commited on
Commit
79a1e5b
1 Parent(s): 85fa60e

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +11 -9
app.py CHANGED
@@ -154,28 +154,30 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, progress=
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  full_path_lora = state_dicts[repo_name]["saved_name"]
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  loaded_state_dict = state_dicts[repo_name]["state_dict"]
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  cross_attention_kwargs = None
 
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  print("Last LoRA:", last_lora, "Was it last merged? ", last_merged, "Was it last fused?", last_fused)
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  print("Current LoRA: ", repo_name)
 
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  if last_lora != repo_name:
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- if last_merged:
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- del pipe
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- gc.collect()
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- pipe = copy.deepcopy(original_pipe)
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- pipe.to(device)
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- elif(last_fused):
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  #pipe.unfuse_lora()
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- pipe.unload_lora_weights()
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  is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
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  if is_compatible:
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  pipe.load_lora_weights(loaded_state_dict)
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- #pipe.fuse_lora(lora_scale)
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  last_fused = True
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  else:
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  is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
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  if(is_pivotal):
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  pipe.load_lora_weights(loaded_state_dict)
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- #pipe.fuse_lora(lora_scale)
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  last_fused = True
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  #Add the textual inversion embeddings from pivotal tuning models
 
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  full_path_lora = state_dicts[repo_name]["saved_name"]
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  loaded_state_dict = state_dicts[repo_name]["state_dict"]
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  cross_attention_kwargs = None
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+
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  print("Last LoRA:", last_lora, "Was it last merged? ", last_merged, "Was it last fused?", last_fused)
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  print("Current LoRA: ", repo_name)
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+
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  if last_lora != repo_name:
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+ #if last_merged:
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+ del pipe
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+ gc.collect()
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+ pipe = copy.deepcopy(original_pipe)
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+ pipe.to(device)
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+ #elif(last_fused):
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  #pipe.unfuse_lora()
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+ #pipe.unload_lora_weights()
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  is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
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  if is_compatible:
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  pipe.load_lora_weights(loaded_state_dict)
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+ pipe.fuse_lora(lora_scale)
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  last_fused = True
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  else:
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  is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
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  if(is_pivotal):
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  pipe.load_lora_weights(loaded_state_dict)
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+ pipe.fuse_lora(lora_scale)
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  last_fused = True
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  #Add the textual inversion embeddings from pivotal tuning models