multimodalart HF staff commited on
Commit
6b7c1b1
β€’
1 Parent(s): ad569d5

Update app.py

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Files changed (1) hide show
  1. app.py +6 -2
app.py CHANGED
@@ -54,7 +54,7 @@ pipe.to(device)
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  last_lora = ""
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  last_merged = False
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-
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  def update_selection(selected_state: gr.SelectData):
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  lora_repo = sdxl_loras[selected_state.index]["repo"]
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  instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
@@ -154,18 +154,20 @@ def run_lora(prompt, negative, lora_scale, selected_state, progress=gr.Progress(
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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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- else:
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  pipe.unload_lora_weights()
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  pipe.unfuse_lora()
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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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  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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  #Add the textual inversion embeddings from pivotal tuning models
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  text_embedding_name = sdxl_loras[selected_state.index]["text_embedding_weights"]
@@ -174,9 +176,11 @@ def run_lora(prompt, negative, lora_scale, selected_state, progress=gr.Progress(
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  embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model")
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  embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
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  embhandler.load_embeddings(embedding_path)
 
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  else:
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  merge_incompatible_lora(full_path_lora, lora_scale)
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  last_merged = True
 
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  image = pipe(
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  prompt=prompt,
 
54
 
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  last_lora = ""
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  last_merged = False
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+ last_fused = False
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  def update_selection(selected_state: gr.SelectData):
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  lora_repo = sdxl_loras[selected_state.index]["repo"]
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  instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
 
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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.unload_lora_weights()
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  pipe.unfuse_lora()
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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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  text_embedding_name = sdxl_loras[selected_state.index]["text_embedding_weights"]
 
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  embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model")
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  embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
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  embhandler.load_embeddings(embedding_path)
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+
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  else:
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  merge_incompatible_lora(full_path_lora, lora_scale)
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  last_merged = True
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+ last_fused=False
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  image = pipe(
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  prompt=prompt,