multimodalart HF staff commited on
Commit
aa6b3a7
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1 Parent(s): c51e24b

Update app.py

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Files changed (1) hide show
  1. app.py +14 -32
app.py CHANGED
@@ -52,6 +52,7 @@ sdxl_loras_raw_new = [item for item in sdxl_loras_raw if item.get("new") == True
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  sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
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  vae = AutoencoderKL.from_pretrained(
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  "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
@@ -184,39 +185,20 @@ def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras, sdxl_lora
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  loaded_state_dict = copy.deepcopy(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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-
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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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-
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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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- text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
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- tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
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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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- last_merged = True
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  image = pipe(
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  prompt=prompt,
 
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  sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
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+ lcm_lora_id = "lcm-sd/lcm-sdxl-base-1.0-lora"
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  vae = AutoencoderKL.from_pretrained(
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  "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
 
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  loaded_state_dict = copy.deepcopy(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_fused):
 
 
 
 
 
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  pipe.unfuse_lora()
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+ pipe.load_lora_weights(loaded_state_dict)
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+ pipe.fuse_lora()
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+ last_fused = True
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+ is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
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+ if(is_pivotal):
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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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+ text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
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+ tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
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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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  image = pipe(
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  prompt=prompt,