Update app.py

app.py

@@ -1,246 +1,31 @@
 import os
-import logging
-
-# Configuração básica de logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-logger.info("Iniciando aplicação")
-
-# Importação do módulo de compatibilidade antes das bibliotecas problemáticas
-try:
-    logger.info("Carregando módulo de compatibilidade HF")
-    import hf_compat
-except Exception as e:
-    logger.error(f"Erro ao carregar módulo de compatibilidade: {e}")
-    raise
-
-# Agora podemos importar as bibliotecas normalmente
-import json
-import copy
 import time
 import random
+import logging
+import traceback
 import numpy as np
-from typing import Any, Dict, List, Optional, Union
-import gradio as gr
 import torch
+import gradio as gr
 from PIL import Image
-import spaces
 import io
-from supabase import create_client, Client
 from datetime import datetime
+from huggingface_hub import HfApi, HfFileSystem, snapshot_download, login
 
-# Imports problemáticos agora devem funcionar
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
-from diffusers.utils import load_image
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
-import requests
-from io import BytesIO
-import datetime
-
-logger.info("Bibliotecas importadas com sucesso")
-
-# Configuração de Logging para facilitar diagnóstico
+# Configurar logging para facilitar debug
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-from transformers import pipeline
-
-from diffusers import (
-    DiffusionPipeline,
-    AutoencoderTiny,
-    AutoencoderKL,
-    AutoPipelineForImage2Image,
-    FluxPipeline,
-    FlowMatchEulerDiscreteScheduler)
-
-logger.info("Carregando transformers e diffusers...")
-
-from huggingface_hub import (
-    hf_hub_download,
-    HfFileSystem,
-    ModelCard,
-    snapshot_download)
-
-from diffusers.utils import load_image
-
-# Inicializa Supabase
-url: str = os.getenv('SUPABASE_URL')
-key: str = os.getenv('SUPABASE_KEY')
-
-try:
-    if url and key:
-        supabase: Client = create_client(url, key)
-        logger.info("Supabase inicializado com sucesso")
-    else:
-        logger.warning("Variáveis de ambiente SUPABASE_URL ou SUPABASE_KEY não configuradas")
-        supabase = None
-except Exception as e:
-    logger.error(f"Erro ao inicializar Supabase: {e}")
-    supabase = None
-
-from huggingface_hub import HfApi
-token = os.getenv("HF_TOKEN")
-
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.16,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-    b = base_shift - m * base_seq_len
-    mu = image_seq_len * m + b
-    return mu
-
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    if timesteps is not None and sigmas is not None:
-        raise ValueError("Apenas um entre `timesteps` ou `sigmas` pode ser passado. Por favor, escolha um para definir valores personalizados")
-    
-    if timesteps is not None:
-        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    elif sigmas is not None:
-        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-        num_inference_steps = len(timesteps)
-    else:
-        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
-        timesteps = scheduler.timesteps
-    return timesteps, num_inference_steps
-
-# FLUX pipeline
-@torch.inference_mode()
-def flux_pipe_call_that_returns_an_iterable_of_images(
-    self,
-    prompt: Union[str, List[str]] = None,
-    prompt_2: Optional[Union[str, List[str]]] = None,
-    height: Optional[int] = None,
-    width: Optional[int] = None,
-    num_inference_steps: int = 32,
-    timesteps: List[int] = None,
-    guidance_scale: float = 3.5,
-    num_images_per_prompt: Optional[int] = 2,
-    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-    latents: Optional[torch.FloatTensor] = None,
-    prompt_embeds: Optional[torch.FloatTensor] = None,
-    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-    output_type: Optional[str] = "pil",
-    return_dict: bool = True,
-    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    max_sequence_length: int = 512,
-    good_vae: Optional[Any] = None,
-):
-    try:
-        height = height or self.default_sample_size * self.vae_scale_factor
-        width = width or self.default_sample_size * self.vae_scale_factor
-        
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            height,
-            width,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            max_sequence_length=max_sequence_length,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-
-        lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
-        prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            max_sequence_length=max_sequence_length,
-            lora_scale=lora_scale,
-        )
-        
-        num_channels_latents = self.transformer.config.in_channels // 4
-        latents, latent_image_ids = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-        
-        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-        image_seq_len = latents.shape[1]
-        mu = calculate_shift(
-            image_seq_len,
-            self.scheduler.config.base_image_seq_len,
-            self.scheduler.config.max_image_seq_len,
-            self.scheduler.config.base_shift,
-            self.scheduler.config.max_shift,
-        )
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps,
-            sigmas,
-            mu=mu,
-        )
-        self._num_timesteps = len(timesteps)
-
-        guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
-
-        for i, t in enumerate(timesteps):
-            if self.interrupt:
-                continue
+# Autenticar com o Hugging Face (necessário para alguns modelos)
+hf_token = os.environ.get("HF_TOKEN")
+if hf_token:
+    login(token=hf_token)
+    logger.info("Autenticado no Hugging Face")
 
-            timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-            noise_pred = self.transformer(
-                hidden_states=latents,
-                timestep=timestep / 1000,
-                guidance=guidance,
-                pooled_projections=pooled_prompt_embeds,
-                encoder_hidden_states=prompt_embeds,
-                txt_ids=text_ids,
-                img_ids=latent_image_ids,
-                joint_attention_kwargs=self.joint_attention_kwargs,
-                return_dict=False,
-            )[0]
-
-            latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-            latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-            image = self.vae.decode(latents_for_image, return_dict=False)[0]
-            yield self.image_processor.postprocess(image, output_type=output_type)[0]
-            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-            torch.cuda.empty_cache()
-            
-        latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-        latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
-        image = good_vae.decode(latents, return_dict=False)[0]
-        self.maybe_free_model_hooks()
-        torch.cuda.empty_cache()
-        yield self.image_processor.postprocess(image, output_type=output_type)[0]
-    except Exception as e:
-        logger.error(f"Erro na função flux_pipe_call_that_returns_an_iterable_of_images: {e}")
-        raise e
+# Define o modelo base que vamos usar
+BASE_MODEL = "black-forest-labs/FLUX.1-dev"
+MAX_SEED = 2**32-1
 
-#------------------------------------------------------------------------------------------------------------------------------------------------------------#
+# Carrega a lista de modelos LoRA disponíveis
 loras = [
     # Super-Realism
     {
@@ -254,7 +39,7 @@ loras = [
         "image": "https://huggingface.co/vcollos/camila/resolve/main/images/1732936378531__000003000_1.jpg",
         "title": "Camila",
         "repo": "vcollos/camila",
-        "weights": "Camila.safetensors",  # Corrigido: removido o 's' extra
+        "weights": "Camila.safetensors",  # Corrigido
         "trigger_word": "A photo of Camila"
     },
     {
@@ -299,26 +84,37 @@ loras = [
         "weights": "lora.safetensors",
         "trigger_word": "A photo of Ditinha"
     }
-    
-    # add new
 ]
 
-# Initialize the base model
-def initialize_models():
-    logger.info("Inicializando modelos...")
-    use_auth_token = True
-    dtype = torch.bfloat16
-    # Verifica se a GPU está disponível
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    logger.info(f"Usando dispositivo: {device}")
-    base_model = "black-forest-labs/FLUX.1-dev"
-
+# Função para inicializar os modelos - carregada apenas quando necessário para economizar memória
+def load_models():
     try:
+        logger.info("Iniciando carregamento dos modelos...")
+        import torch
+        from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
+        
+        # Configurar dispositivo e tipo de dados
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        dtype = torch.bfloat16 if device == "cuda" else torch.float32
+        
+        logger.info(f"Usando dispositivo: {device} com dtype: {dtype}")
+        
+        # Carregar o autoencoder tiny para previsualização
         taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-        good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
-        pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
+        
+        # Carregar o VAE completo para a imagem final
+        good_vae = AutoencoderKL.from_pretrained(BASE_MODEL, subfolder="vae", torch_dtype=dtype).to(device)
+        
+        # Carregar o pipeline principal
+        pipe = DiffusionPipeline.from_pretrained(
+            BASE_MODEL,
+            torch_dtype=dtype,
+            vae=taef1
+        ).to(device)
+        
+        # Criar pipeline de image-to-image
         pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
-            base_model,
+            BASE_MODEL,
             vae=good_vae,
             transformer=pipe.transformer,
             text_encoder=pipe.text_encoder,
@@ -326,66 +122,43 @@ def initialize_models():
             text_encoder_2=pipe.text_encoder_2,
             tokenizer_2=pipe.tokenizer_2,
             torch_dtype=dtype
-        )
+        ).to(device)
         
-        # Monkeypatch da função de pipeline
-        pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+        logger.info("Modelos carregados com sucesso")
         
-        logger.info("Modelos inicializados com sucesso")
-        return pipe, pipe_i2i, good_vae, device
+        return {
+            "pipe": pipe,
+            "pipe_i2i": pipe_i2i,
+            "good_vae": good_vae,
+            "device": device,
+            "dtype": dtype
+        }
     except Exception as e:
-        logger.error(f"Erro ao inicializar modelos: {e}")
+        logger.error(f"Erro ao carregar modelos: {e}")
+        logger.error(traceback.format_exc())
         raise e
 
-# Inicialização dos modelos
-pipe, pipe_i2i, good_vae, device = initialize_models()
-
-MAX_SEED = 2**32-1
-
-def upload_image_to_supabase(image, filename):
-    """ Faz upload da imagem para o Supabase Storage e retorna a URL pública. """
-    if not supabase:
-        logger.warning("Supabase não inicializado. Não será feito upload da imagem.")
-        return None
+# Classe para medir duração de operações
+class TimeMeasure:
+    def __init__(self, name=""):
+        self.name = name
         
-    img_bytes = io.BytesIO()
-    image.save(img_bytes, format="PNG")
-    img_bytes.seek(0)  # Move para o início do arquivo
-
-    storage_path = f"images/{filename}"
-
-    try:
-        # Faz upload da imagem para o Supabase
-        supabase.storage.from_("images").upload(storage_path, img_bytes.getvalue(), {"content-type": "image/png"})
-
-        # Retorna a URL pública da imagem
-        base_url = f"{url}/storage/v1/object/public/images"
-        return f"{base_url}/{filename}"
-    except Exception as e:
-        logger.error(f"Erro no upload da imagem: {e}")
-        return None
-
-class calculateDuration:
-    def __init__(self, activity_name=""):
-        self.activity_name = activity_name
-
     def __enter__(self):
-        self.start_time = time.time()
+        self.start = time.time()
         return self
-    
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.end_time = time.time()
-        self.elapsed_time = self.end_time - self.start_time
-        if self.activity_name:
-            logger.info(f"Tempo para {self.activity_name}: {self.elapsed_time:.6f} segundos")
-        else:
-            logger.info(f"Tempo passado: {self.elapsed_time:.6f} segundos")
+        
+    def __exit__(self, *args):
+        self.duration = time.time() - self.start
+        logger.info(f"🕒 {self.name}: {self.duration:.2f} segundos")
 
+# Função para processar a seleção de modelos na interface
 def update_selection(evt: gr.SelectData, width, height):
     selected_lora = loras[evt.index]
     new_placeholder = f"Digite o prompt para {selected_lora['title']}, de preferência em inglês."
     lora_repo = selected_lora["repo"]
     updated_text = f"### Selecionado: [{lora_repo}](https://huggingface.co/{lora_repo}) ✅"
+    
+    # Ajustar dimensões conforme especificações do modelo
     if "aspect" in selected_lora:
         if selected_lora["aspect"] == "retrato":
             width = 768
@@ -396,6 +169,7 @@ def update_selection(evt: gr.SelectData, width, height):
         else:
             width = 1024
             height = 1024
+            
     return (
         gr.update(placeholder=new_placeholder),
         updated_text,
@@ -404,384 +178,418 @@ def update_selection(evt: gr.SelectData, width, height):
         height,
     )
 
-@spaces.GPU(duration=60)  # Reduzido de 100 para 60 para melhor gerenciamento de recursos
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
-    try:
-        logger.info(f"Gerando imagem com prompt: {prompt_mash[:50]}...")
-        pipe.to("cuda")
-        generator = torch.Generator(device="cuda").manual_seed(seed)
-        with calculateDuration("Generating image"):
-            # Generate image
-            for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-                prompt=prompt_mash,
-                num_inference_steps=steps,
-                guidance_scale=cfg_scale,
-                width=width,
-                height=height,
-                generator=generator,
-                joint_attention_kwargs={"scale": lora_scale},
-                output_type="pil",
-                good_vae=good_vae,
-            ):
-                yield img
-    except Exception as e:
-        logger.error(f"Erro ao gerar imagem: {e}")
-        raise gr.Error(f"Erro ao gerar imagem: {str(e)}")
-
-@spaces.GPU(duration=60)  # Reduzido para melhor gerenciamento
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
-    try:
-        logger.info(f"Gerando image-to-image com prompt: {prompt_mash[:50]}...")
-        generator = torch.Generator(device="cuda").manual_seed(seed)
-        pipe_i2i.to("cuda")
-        image_input = load_image(image_input_path)
-        final_image = pipe_i2i(
-            prompt=prompt_mash,
-            image=image_input,
-            strength=image_strength,
-            num_inference_steps=steps,
-            guidance_scale=cfg_scale,
-            width=width,
-            height=height,
-            generator=generator,
-            joint_attention_kwargs={"scale": lora_scale},
-            output_type="pil",
-        ).images[0]
-        return final_image
-    except Exception as e:
-        logger.error(f"Erro ao gerar image-to-image: {e}")
-        raise gr.Error(f"Erro ao gerar image-to-image: {str(e)}")
-
-def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+# Função principal de geração de imagens
+def generate_image(prompt, steps, cfg_scale, width, height, selected_index, randomize_seed, seed, lora_scale, input_image=None, image_strength=0.75, progress=gr.Progress()):
     try:
         if selected_index is None:
-            raise gr.Error("Selecione um modelo para continuar.")
+            raise gr.Error("Por favor, selecione um modelo LoRA primeiro")
         
+        # Carregar modelos (apenas quando necessário)
+        models = load_models()
+        pipe = models["pipe"]
+        pipe_i2i = models["pipe_i2i"] 
+        good_vae = models["good_vae"]
+        device = models["device"]
+        
+        # Preparar prompt com trigger words
         selected_lora = loras[selected_index]
         lora_path = selected_lora["repo"]
-        trigger_word = selected_lora["trigger_word"]
+        trigger_word = selected_lora.get("trigger_word", "")
         qualidade = "<flux.1-dev>"
         
-        logger.info(f"Usando modelo: {lora_path}")
-        
         if trigger_word:
-            if "trigger_position" in selected_lora:
-                if selected_lora["trigger_position"] == "prepend":
-                    prompt_mash = f"{trigger_word} {prompt} {qualidade}"
-                else:
-                    prompt_mash = f"{prompt} {trigger_word} {qualidade}"
+            trigger_position = selected_lora.get("trigger_position", "prepend")
+            if trigger_position == "prepend":
+                prompt_full = f"{trigger_word} {prompt} {qualidade}"
             else:
-                prompt_mash = f"{trigger_word} {prompt} {qualidade}"
+                prompt_full = f"{prompt} {trigger_word} {qualidade}"
         else:
-            prompt_mash = prompt
-
-        with calculateDuration("Carregando Modelo"):
-            pipe.unload_lora_weights()
-            pipe_i2i.unload_lora_weights()
-            torch.cuda.empty_cache()  # Limpeza explícita da memória
+            prompt_full = f"{prompt} {qualidade}"
             
-        # LoRA weights flow
-        with calculateDuration(f"Carregando modelo para {selected_lora['title']}"):
-            pipe_to_use = pipe_i2i if image_input is not None else pipe
+        logger.info(f"Prompt completo: {prompt_full}")
+        
+        # Randomizar seed se necessário
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+            
+        # Configurar gerador
+        generator = torch.Generator(device=device).manual_seed(seed)
+        progress(0, desc="Preparando modelo...")
+            
+        # Descarregar pesos LoRA anteriores e limpar cache
+        with TimeMeasure("Descarregando modelos anteriores"):
+            pipe.unload_lora_weights() if hasattr(pipe, 'unload_lora_weights') else None
+            pipe_i2i.unload_lora_weights() if hasattr(pipe_i2i, 'unload_lora_weights') else None
+            torch.cuda.empty_cache()
+
+        # Carregar pesos LoRA
+        with TimeMeasure(f"Carregando LoRA {selected_lora['title']}"):
+            pipeline_to_use = pipe_i2i if input_image is not None else pipe
             weight_name = selected_lora.get("weights", None)
             
             try:
-                pipe_to_use.load_lora_weights(
-                    lora_path, 
-                    weight_name=weight_name, 
+                pipeline_to_use.load_lora_weights(
+                    lora_path,
+                    weight_name=weight_name,
                     low_cpu_mem_usage=True
                 )
-                logger.info(f"LoRA carregado com sucesso: {weight_name}")
+                logger.info(f"LoRA carregado: {weight_name}")
             except Exception as e:
                 logger.error(f"Erro ao carregar LoRA: {e}")
-                raise gr.Error(f"Erro ao carregar o modelo: {str(e)}. Verifique o nome do arquivo e o caminho.")
-                
-        with calculateDuration("Gerando fontes"):
-            if randomize_seed:
-                seed = random.randint(0, MAX_SEED)
-                
-        if image_input is not None:
-            final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
-            yield final_image, seed, gr.update(visible=False)
-        else:
-            image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
+                raise gr.Error(f"Erro ao carregar LoRA: {str(e)}")
         
-            final_image = None
-            step_counter = 0
-            try:
-                for image in image_generator:
-                    step_counter += 1
-                    final_image = image
-                    progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-                    yield image, seed, gr.update(value=progress_bar, visible=True)
-            except Exception as e:
-                logger.error(f"Erro durante geração de imagens: {e}")
-                raise gr.Error(f"Erro durante geração: {str(e)}")
+        # Gerar imagem
+        with TimeMeasure("Gerando imagem"):
+            progress(0.2, desc=f"Gerando imagem com {steps} passos...")
+            
+            if input_image is not None:
+                # Modo image-to-image
+                from diffusers.utils import load_image
+                image_input = load_image(input_image)
                 
-            yield final_image, seed, gr.update(value=progress_bar, visible=False)
-
-        # Salvar a imagem no Supabase
-        if final_image and supabase:
-            filename = f"image_{seed}_{datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S')}.png"
-            image_url = upload_image_to_supabase(final_image, filename)
-            if image_url:
-                logger.info(f"Imagem salva no Supabase: {image_url}")
-                # Salvar metadados no Supabase
-                try:
-                    response = supabase.table("images").insert({
-                        "prompt": prompt_mash,
-                        "cfg_scale": cfg_scale,
-                        "steps": steps,
-                        "seed": seed,
-                        "lora_scale": lora_scale,
-                        "image_url": image_url,
-                        "created_at": datetime.datetime.utcnow().isoformat()
-                    }).execute()
-                    
-                    if response.data:
-                        logger.info("Metadados salvos no Supabase")
-                    else:
-                        logger.warning("Resposta vazia do Supabase")
-                except Exception as e:
-                    logger.error(f"Erro ao salvar metadados no Supabase: {e}")
+                result = pipe_i2i(
+                    prompt=prompt_full,
+                    image=image_input,
+                    strength=image_strength,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    joint_attention_kwargs={"scale": lora_scale},
+                )
+                final_image = result.images[0]
             else:
-                logger.warning("URL da imagem retornou None")
+                # Modo text-to-image com visualização progressiva
+                final_image = None
+                
+                # Definir função de callback para atualizar o progresso
+                def callback_fn(i, t, latents):
+                    progress((i + 1) / steps, desc=f"Passo {i+1}/{steps}")
+                    return True
+                
+                # Generate image
+                result = pipe(
+                    prompt=prompt_full,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    joint_attention_kwargs={"scale": lora_scale},
+                    callback=callback_fn,
+                    callback_steps=1
+                )
+                final_image = result.images[0]
+        
+        # Limpar cache após geração
+        torch.cuda.empty_cache()
+            
+        return final_image, seed
+        
     except Exception as e:
-        logger.error(f"Erro na função run_lora: {e}")
+        logger.error(f"Erro ao gerar imagem: {e}")
+        logger.error(traceback.format_exc())
         raise gr.Error(f"Erro: {str(e)}")
 
-def get_huggingface_safetensors(link):
+# Verifica modelo personalizado
+def add_custom_lora(custom_lora):
+    global loras
+    
+    if not custom_lora:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+    
     try:
-        split_link = link.split("/")
-        if(len(split_link) == 2):
-            model_card = ModelCard.load(link)
+        # Processar o link ou ID do modelo
+        model_id = custom_lora
+        if model_id.startswith("https://huggingface.co/"):
+            model_id = model_id.replace("https://huggingface.co/", "")
+            
+        logger.info(f"Verificando modelo: {model_id}")
+        
+        # Verificar se é um modelo FLUX LoRA válido
+        fs = HfFileSystem()
+        
+        # Verificar card do modelo
+        try:
+            from huggingface_hub import ModelCard
+            model_card = ModelCard.load(model_id)
             base_model = model_card.data.get("base_model")
-            logger.info(f"Modelo base: {base_model}")
-      
-            #Allows Both
-            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
-                raise Exception("Flux LoRA Not Found!")
-                
-            # Only allow "black-forest-labs/FLUX.1-dev"
-            #if base_model != "black-forest-labs/FLUX.1-dev":
-                #raise Exception("Only FLUX.1-dev is supported, other LoRA models are not allowed!")
+            
+            if base_model != "black-forest-labs/FLUX.1-dev" and base_model != "black-forest-labs/FLUX.1-schnell":
+                raise gr.Error("Este modelo não é um LoRA do FLUX")
                 
-            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
+            title = model_id.split("/")[-1]
             trigger_word = model_card.data.get("instance_prompt", "")
-            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
-            fs = HfFileSystem()
-            safetensors_name = None
-            try:
-                list_of_files = fs.ls(link, detail=False)
-                for file in list_of_files:
-                    if(file.endswith(".safetensors")):
-                        safetensors_name = file.split("/")[-1]
-                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
-                      image_elements = file.split("/")
-                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
-            except Exception as e:
-                logger.error(f"Erro ao listar arquivos: {e}")
-                raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
-                
-            if not safetensors_name:
-                raise Exception("Nenhum arquivo .safetensors encontrado no repositório")
-                
-            return split_link[1], link, safetensors_name, trigger_word, image_url
-    except Exception as e:
-        logger.error(f"Erro em get_huggingface_safetensors: {e}")
-        raise e
-
-def check_custom_model(link):
-    try:
-        if(link.startswith("https://")):
-            if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
-                link_split = link.split("huggingface.co/")
-                return get_huggingface_safetensors(link_split[1])
-        else: 
-            return get_huggingface_safetensors(link)
-    except Exception as e:
-        logger.error(f"Erro em check_custom_model: {e}")
-        raise e
-
-def add_custom_lora(custom_lora):
-    global loras
-    if custom_lora:
+            
+            # Encontrar imagem de exemplo
+            card_image = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", "")
+            image_url = f"https://huggingface.co/{model_id}/resolve/main/{card_image}" if card_image else None
+            
+        except Exception as e:
+            logger.warning(f"Erro ao carregar card: {e}, tentando método alternativo")
+            title = model_id.split("/")[-1]
+            trigger_word = ""
+            image_url = None
+            
+        # Encontrar arquivo de pesos e imagem
+        weight_file = None
         try:
-            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
-            logger.info(f"Modelo Externo: {repo}")
-            card = f'''
-            <div class="custom_lora_card">
-              <span>Loaded custom LoRA:</span>
-              <div class="card_internal">
-                <img src="{image}" />
-                <div>
-                    <h3>{title}</h3>
-                   <small>{"Usando: <code><b>"+trigger_word+"</code></b> como palavra-chave" if trigger_word else "Não encontramos a palavra-chave, se tiver, coloque-a no prompt."}<br></small>
-                </div>
-              </div>
-           </div>
-           '''
-            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
-            if not existing_item_index:
-                new_item = {
-                    "image": image,
-                    "title": title,
-                    "repo": repo,
-                    "weights": path,
-                    "trigger_word": trigger_word,
-                }
-                logger.info(f"Novo item: {new_item}")
-                existing_item_index = len(loras)
-                loras.append(new_item)
-
-            return (
-                gr.update(visible=True, value=card),
-                gr.update(visible=True),
-                gr.Gallery(selected_index=None),
-                f"Custom: {path}",
-                existing_item_index,
-                trigger_word,
-            )
+            files = fs.ls(model_id, detail=False)
+            
+            for file in files:
+                filename = file.split("/")[-1]
+                
+                # Encontrar arquivo de pesos
+                if filename.endswith(".safetensors"):
+                    weight_file = filename
+                    
+                # Encontrar imagem se não encontrada no card
+                if not image_url and filename.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
+                    image_url = f"https://huggingface.co/{model_id}/resolve/main/{filename}"
+                    
         except Exception as e:
-            error_msg = f"Modelo Inválido: ou o link está errado ou não é um FLUX. Erro: {str(e)}"
-            logger.error(error_msg)
-            gr.Warning(error_msg)
-            return (
-                gr.update(visible=True, value=f"Modelo Inválido: ou o link está errado ou não é um FLUX"),
-                gr.update(visible=False),
-                gr.update(),
-                "",
-                None,
-                "",
-            )
-    else:
-        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+            logger.error(f"Erro ao listar arquivos: {e}")
+            raise gr.Error(f"Não foi possível acessar o repositório: {str(e)}")
+            
+        if not weight_file:
+            raise gr.Error("Nenhum arquivo .safetensors encontrado no repositório")
+            
+        # Se não encontrou imagem, usar uma placeholder
+        if not image_url:
+            image_url = "https://huggingface.co/front/assets/huggingface_logo-noborder.svg"
+            
+        # Criar card HTML
+        card = f'''
+        <div class="custom_lora_card">
+          <span>LoRA carregado com sucesso:</span>
+          <div class="card_internal">
+            <img src="{image_url}" style="max-width: 100px; max-height: 100px;"/>
+            <div>
+                <h3>{title}</h3>
+               <small>{"Usando: <code><b>"+trigger_word+"</code></b> como palavra-chave" if trigger_word else "Não encontramos a palavra-chave, se tiver, coloque-a no prompt."}<br></small>
+            </div>
+          </div>
+       </div>
+       '''
+       
+        # Verificar se já existe na lista
+        existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == model_id), None)
+        
+        if existing_item_index is None:
+            new_item = {
+                "image": image_url,
+                "title": title,
+                "repo": model_id,
+                "weights": weight_file,
+                "trigger_word": trigger_word,
+            }
+            
+            existing_item_index = len(loras)
+            loras.append(new_item)
+            logger.info(f"Adicionado novo modelo: {title}")
+            
+        return (
+            gr.update(visible=True, value=card),
+            gr.update(visible=True),
+            gr.Gallery(value=[(item["image"], item["title"]) for item in loras]),
+            f"Modelo: {title}",
+            existing_item_index,
+            trigger_word if trigger_word else "",
+        )
+        
+    except Exception as e:
+        logger.error(f"Erro ao adicionar modelo: {e}")
+        error_msg = f"Modelo inválido: {str(e)}"
+        return (
+            gr.update(visible=True, value=error_msg),
+            gr.update(visible=False),
+            gr.update(),
+            "",
+            None,
+            "",
+        )
 
 def remove_custom_lora():
-     return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
-
-# Remova esta linha, pois você já está usando spaces.GPU
-# run_lora.zerogpu = True
-
-collos = gr.themes.Soft(
-    primary_hue="gray",
-    secondary_hue="stone",
-    neutral_hue="slate",
-    radius_size=gr.themes.Size(lg="15px", md="8px", sm="6px", xl="16px", xs="4px", xxl="24px", xxs="2px")
-).set(
-    body_background_fill='*primary_100',
-    embed_radius='*radius_lg',
-    shadow_drop='0 1px 2px rgba(0, 0, 0, 0.1)',
-    shadow_drop_lg='0 1px 2px rgba(0, 0, 0, 0.1)',
-    shadow_inset='0 1px 2px rgba(0, 0, 0, 0.1)',
-    shadow_spread='0 1px 2px rgba(0, 0, 0, 0.1)',
-    shadow_spread_dark='0 1px 2px rgba(0, 0, 0, 0.1)',
-    block_radius='*radius_lg',
-    block_shadow='*shadow_drop',
-    container_radius='*radius_lg'
-)
-
-collos.css = """
-#group_with_padding {
-    padding: 20px;
-    background-color: #f5f5f5;
-    border: 1px solid #ccc;
-}
-
-#padded_text {
-    padding: 10px;
-    background-color: #eef;
-    border-radius: 5px;
-    font-size: 16px;
-}
-
-.progress-container {
-    width: 100%;
-    background-color: #f1f1f1;
-    border-radius: 5px;
-    margin: 10px 0;
-}
-
-.progress-bar {
-    width: calc(var(--current) / var(--total) * 100%);
-    height: 20px;
-    background-color: #4CAF50;
-    border-radius: 5px;
-    text-align: center;
-    line-height: 20px;
-    color: white;
-}
-"""
-
-with gr.Blocks(theme=collos, delete_cache=(60, 60)) as app:
-    title = gr.HTML(
-        """<img src="https://huggingface.co/spaces/vcollos/Uniodonto/resolve/main/logo/logo_collos_3.png" alt="Logo" style="display: block; margin: 0 auto; padding: 5px 0px 20px 0px; width: 200px;" />""",
-        elem_id="title",
+    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
+
+# Interface Gradio
+def create_interface():
+    # Tema personalizado
+    collos = gr.themes.Soft(
+        primary_hue="gray",
+        secondary_hue="stone",
+        neutral_hue="slate",
+        radius_size=gr.themes.Size(lg="15px", md="8px", sm="6px", xl="16px", xs="4px", xxl="24px", xxs="2px")
+    ).set(
+        body_background_fill='*primary_100',
+        embed_radius='*radius_lg',
+        shadow_drop='0 1px 2px rgba(0, 0, 0, 0.1)',
+        shadow_drop_lg='0 1px 2px rgba(0, 0, 0, 0.1)',
+        shadow_inset='0 1px 2px rgba(0, 0, 0, 0.1)',
+        shadow_spread='0 1px 2px rgba(0, 0, 0, 0.1)',
+        shadow_spread_dark='0 1px 2px rgba(0, 0, 0, 0.1)',
+        block_radius='*radius_lg',
+        block_shadow='*shadow_drop',
+        container_radius='*radius_lg'
     )
-    selected_index = gr.State(None)
-    with gr.Row():
-        with gr.Column(scale=3):
-            prompt = gr.Textbox(label="Prompt", lines=1, placeholder=":/ Selecione o modelo ")
-        with gr.Column(scale=1):
-            generate_button = gr.Button("Gerar Imagem", variant="primary", elem_id="cta")
-    with gr.Row():
-        with gr.Column():
-            selected_info = gr.Markdown("")
-            gallery = gr.Gallery(
-                label="Galeria",
-                value=[(item["image"], item["title"]) for item in loras],  # Argumento nomeado como 'value'
-                allow_preview=False,
-                columns=3,
-                show_share_button=False
-            )
-            with gr.Group():
-                custom_lora = gr.Textbox(label="Selecione um Modelo Externo", placeholder="black-forest-labs/FLUX.1-dev")
-                gr.Markdown("[Cheque a lista de modelos do Huggingface](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
-            custom_lora_info = gr.HTML(visible=False)
-            custom_lora_button = gr.Button("Remova modelo Externo", visible=False)
-        with gr.Column():
-            progress_bar = gr.Markdown(elem_id="progress", visible=False)
-            result = gr.Image(label="Imagem Gerada")
 
-    with gr.Row():
-        with gr.Accordion("Configurações Avançadas", open=False):
-            with gr.Row():
-                input_image = gr.Image(label="Insira uma Imagem", type="filepath")
-                image_strength = gr.Slider(label="Remossão de ruído", info="Valores mais baixos significam maior influência da imagem.", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
+    # CSS personalizado
+    css = """
+    #group_with_padding {
+        padding: 20px;
+        background-color: #f5f5f5;
+        border: 1px solid #ccc;
+    }
+
+    #padded_text {
+        padding: 10px;
+        background-color: #eef;
+        border-radius: 5px;
+        font-size: 16px;
+    }
+    
+    .custom_lora_card {
+        padding: 10px;
+        background-color: #f5f5f5;
+        border-radius: 10px;
+        margin-top: 10px;
+    }
+    
+    .card_internal {
+        display: flex;
+        align-items: center;
+        margin-top: 10px;
+    }
+    
+    .card_internal img {
+        margin-right: 15px;
+        border-radius: 5px;
+    }
+    """
+
+    # Interface principal
+    with gr.Blocks(theme=collos, css=css) as interface:
+        # Logo
+        title = gr.HTML(
+            """<img src="https://huggingface.co/spaces/vcollos/Uniodonto/resolve/main/logo/logo_collos_3.png" alt="Logo" style="display: block; margin: 0 auto; padding: 5px 0px 20px 0px; width: 200px;" />""",
+            elem_id="title",
+        )
+        
+        # Estado para armazenar o índice do modelo selecionado
+        selected_index = gr.State(None)
+        
+        # Seção principal
+        with gr.Row():
+            with gr.Column(scale=3):
+                prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Selecione um modelo primeiro")
+            with gr.Column(scale=1):
+                generate_button = gr.Button("Gerar Imagem", variant="primary", elem_id="cta")
+                
+        # Galeria e resultados
+        with gr.Row():
             with gr.Column():
-                with gr.Row():
-                    cfg_scale = gr.Slider(label="Aumentar Escala", minimum=1, maximum=20, step=0.5, value=3.0)
-                    steps = gr.Slider(label="Passos", minimum=1, maximum=50, step=1, value=32)
+                selected_info = gr.Markdown("")
+                gallery = gr.Gallery(
+                    label="Modelos Disponíveis",
+                    value=[(item["image"], item["title"]) for item in loras],
+                    allow_preview=False,
+                    columns=3,
+                    show_share_button=False
+                )
                 
-                with gr.Row():
-                    width = gr.Slider(label="Largura", minimum=256, maximum=1536, step=64, value=1024)
-                    height = gr.Slider(label="Altura", minimum=256, maximum=1536, step=64, value=1024)
+                # Seção LoRA personalizado
+                with gr.Group():
+                    custom_lora = gr.Textbox(
+                        label="Adicionar Modelo Externo", 
+                        placeholder="Nome do modelo ou URL (ex: vcollos/VitorCollos)"
+                    )
+                    gr.Markdown(
+                        "[Ver modelos FLUX no Hugging Face](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", 
+                        elem_id="lora_list"
+                    )
+                
+                # Informações do modelo personalizado
+                custom_lora_info = gr.HTML(visible=False)
+                custom_lora_button = gr.Button("Remover Modelo Externo", visible=False)
                 
+            # Resultado da geração
+            with gr.Column():
+                result = gr.Image(label="Imagem Gerada", type="pil")
+                seed_output = gr.Number(label="Seed", precision=0)
+                
+        # Configurações avançadas
+        with gr.Row():
+            with gr.Accordion("Configurações Avançadas", open=False):
                 with gr.Row():
-                    randomize_seed = gr.Checkbox(True, label="Fonte Randomizada")
-                    seed = gr.Slider(label="Fontes", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
-                    lora_scale = gr.Slider(label="Escala do Modelo", minimum=0, maximum=3, step=0.01, value=1.20)
-
-    gallery.select(
-        update_selection,
-        inputs=[width, height],
-        outputs=[prompt, selected_info, selected_index, width, height]
-    )
-    custom_lora.input(
-        add_custom_lora,
-        inputs=[custom_lora],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
-   )
-    custom_lora_button.click(
-        remove_custom_lora,
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
-    )
-    gr.on(
-        triggers=[generate_button.click, prompt.submit],
-        fn=run_lora,
-        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
-        outputs=[result, seed, progress_bar]
-    )
+                    input_image = gr.Image(label="Imagem de Referência (opcional)", type="filepath")
+                    image_strength = gr.Slider(
+                        label="Força da Imagem Original", 
+                        info="Valores menores preservam mais da imagem original", 
+                        minimum=0.1, 
+                        maximum=1.0, 
+                        step=0.01, 
+                        value=0.75
+                    )
+                
+                with gr.Column():
+                    with gr.Row():
+                        cfg_scale = gr.Slider(label="Escala de Orientação (CFG)", minimum=1, maximum=20, step=0.5, value=3.0)
+                        steps = gr.Slider(label="Passos de Inferência", minimum=1, maximum=50, step=1, value=32)
+                    
+                    with gr.Row():
+                        width = gr.Slider(label="Largura", minimum=256, maximum=1536, step=64, value=1024)
+                        height = gr.Slider(label="Altura", minimum=256, maximum=1536, step=64, value=1024)
+                    
+                    with gr.Row():
+                        randomize_seed = gr.Checkbox(True, label="Seed Aleatória")
+                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
+                        lora_scale = gr.Slider(label="Intensidade do LoRA", minimum=0, maximum=3, step=0.01, value=1.20)
+        
+        # Eventos
+        gallery.select(
+            update_selection,
+            inputs=[width, height],
+            outputs=[prompt, selected_info, selected_index, width, height]
+        )
+        
+        custom_lora.change(
+            add_custom_lora,
+            inputs=[custom_lora],
+            outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
+        )
+        
+        custom_lora_button.click(
+            remove_custom_lora,
+            outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
+        )
+        
+        generate_inputs = [
+            prompt, steps, cfg_scale, width, height, selected_index, 
+            randomize_seed, seed, lora_scale, input_image, image_strength
+        ]
+        
+        generate_outputs = [result, seed_output]
+        
+        generate_button.click(generate_image, inputs=generate_inputs, outputs=generate_outputs)
+        prompt.submit(generate_image, inputs=generate_inputs, outputs=generate_outputs)
+        
+        # Informações iniciais
+        gr.Markdown(
+            """
+            ## Gerador de Imagens com FLUX LoRA
+            
+            1. Selecione um modelo na galeria
+            2. Digite um prompt (preferencialmente em inglês)
+            3. Clique em "Gerar Imagem"
+            
+            Use as configurações avançadas para ajustar parâmetros como tamanho, número de passos, etc.
+            """
+        )
+        
+    return interface
 
-app.queue(concurrency_count=1)  # Limitar a concorrência para evitar problemas de memória
-app.launch()
+# Criar interface e iniciar
+if __name__ == "__main__":
+    app = create_interface()
+    app.queue(concurrency_count=1).launch(debug=True)