multimodalart HF staff commited on
Commit
3dccffc
1 Parent(s): 79a1e5b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +9 -13
app.py CHANGED
@@ -154,19 +154,15 @@ 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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-
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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:
@@ -187,6 +183,7 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, 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_fused=False
@@ -198,7 +195,6 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, progress=
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  width=1024,
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  height=1024,
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  num_inference_steps=20,
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- cross_attention_kwargs = {"scale" : lora_scale},
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  guidance_scale=7.5,
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  ).images[0]
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  last_lora = repo_name
 
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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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  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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  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_fused=False
 
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  width=1024,
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  height=1024,
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  num_inference_steps=20,
 
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  guidance_scale=7.5,
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  ).images[0]
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  last_lora = repo_name