Update app.py
Browse files
app.py
CHANGED
@@ -244,73 +244,41 @@ loras = [
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# add new
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#
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device = "cuda" if torch.cuda.is_available() else "cpu"
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else:
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print(f"{local_path} já existe.")
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# Baixar arquivos necessários
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download_file(config_url, local_config_path)
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download_file(vae_weights_url, vae_weights_path)
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# Carregar o Autoencoder com config.json local
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try:
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print("Carregando o Autoencoder...")
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good_vae = AutoencoderKL.from_pretrained(
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pretrained_model_name_or_path=local_config_path, # Config local
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filename=vae_weights_path, # Pesos locais
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torch_dtype=dtype
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).to(device)
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print("Autoencoder carregado com sucesso!")
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except Exception as e:
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print(f"Erro ao carregar o Autoencoder: {e}")
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good_vae = None
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# Configurar o pipeline principal manualmente
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try:
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print("Configurando o pipeline principal...")
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pipe = DiffusionPipeline.from_pretrained(
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pretrained_model_name_or_path=local_config_path, # Config local
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torch_dtype=dtype,
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vae=good_vae
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).to(device)
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print("Pipeline principal configurado com sucesso!")
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except Exception as e:
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print(f"Erro ao configurar o pipeline principal: {e}")
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pipe = None
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# Configurar o pipeline Image-to-Image
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try:
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print("Configurando o pipeline Image-to-Image...")
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pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
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pretrained_model_or_path=local_config_path,
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vae=good_vae,
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torch_dtype=dtype
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)
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print("Pipeline Image-to-Image configurado com sucesso!")
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except Exception as e:
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print(f"Erro ao configurar o pipeline Image-to-Image: {e}")
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MAX_SEED = 2**32-1
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# add new
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]
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# Initialize the base model
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use_auth_token=True
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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base_model = "black-forest-labs/FLUX.1-dev"
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
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pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
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base_model,
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vae=good_vae,
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transformer=pipe.transformer,
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text_encoder=pipe.text_encoder,
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tokenizer=pipe.tokenizer,
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text_encoder_2=pipe.text_encoder_2,
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tokenizer_2=pipe.tokenizer_2,
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torch_dtype=dtype
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)
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#TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.#
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
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pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
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vae=good_vae,
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transformer=pipe.transformer,
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text_encoder=pipe.text_encoder,
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tokenizer=pipe.tokenizer,
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text_encoder_2=pipe.text_encoder_2,
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tokenizer_2=pipe.tokenizer_2,
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torch_dtype=dtype
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)
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MAX_SEED = 2**32-1
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