Update app.py

app.py

@@ -8,9 +8,8 @@ import gradio as gr
 from huggingface_hub import hf_hub_download
 import spaces
 from typing import Union, Sequence, Mapping, Any
-import asyncio
 
-# Diagnóstico CUDA
+# Configuração inicial e diagnóstico CUDA
 print("Python version:", sys.version)
 print("Torch version:", torch.__version__)
 print("CUDA disponível:", torch.cuda.is_available())
@@ -24,11 +23,18 @@ comfyui_path = os.path.join(current_dir, "ComfyUI")
 sys.path.append(comfyui_path)
 
 # Importar ComfyUI components
-from nodes import NODE_CLASS_MAPPINGS, init_custom_nodes
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "ComfyUI/comfy"))
+
+import comfy.diffusers_load
+import comfy.samplers
+import comfy.sample
+import comfy.sd
+import comfy.utils
+from comfy.cli_args import args
 import folder_paths
-from comfy import model_management
-import execution
-import server
+
+# Importar nós do ComfyUI
+from nodes import CLIPTextEncode, VAEDecode, EmptyLatentImage, VAEEncode
 
 # Configuração de diretórios
 BASE_DIR = os.path.dirname(os.path.realpath(__file__))
@@ -36,13 +42,6 @@ output_dir = os.path.join(BASE_DIR, "output")
 os.makedirs(output_dir, exist_ok=True)
 folder_paths.set_output_directory(output_dir)
 
-# Inicializar o servidor e os nós
-loop = asyncio.new_event_loop()
-asyncio.set_event_loop(loop)
-server_instance = server.PromptServer(loop)
-execution.PromptQueue(server_instance)
-init_custom_nodes()
-
 # Helper function
 def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
     try:
@@ -50,7 +49,7 @@ def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
     except KeyError:
         return obj["result"][index]
 
-# Baixar modelos necessários
+# Baixar modelos
 def download_models():
     print("Baixando modelos...")
     models = [
@@ -58,9 +57,8 @@ def download_models():
         ("comfyanonymous/flux_text_encoders", "t5xxl_fp16.safetensors", "models/text_encoders"),
         ("zer0int/CLIP-GmP-ViT-L-14", "ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors", "models/text_encoders"),
         ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/vae"),
-        ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/diffusion_models"),  # Corrigido aqui
-        ("google/siglip-so400m-patch14-384", "model.safetensors", "models/clip_vision"),
-        ("nftnik/NFTNIK-FLUX.1-dev-LoRA", "NFTNIK_V1.safetensors", "models/lora")
+        ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/diffusion_models"),
+        ("google/siglip-so400m-patch14-384", "model.safetensors", "models/clip_vision")
     ]
     
     for repo_id, filename, local_dir in models:
@@ -70,88 +68,37 @@ def download_models():
             hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
         except Exception as e:
             print(f"Erro ao baixar {filename} de {repo_id}: {str(e)}")
-            # Continue mesmo se um download falhar
             continue
 
-# Download models antes de inicializar
+# Download models no início
 download_models()
 
 # Inicializar modelos
 print("Inicializando modelos...")
 with torch.inference_mode():
-    # Initialize nodes
-    intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
-    dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
-    dualcliploader_357 = dualcliploader.load_clip(
-        clip_name1="models/text_encoders/t5xxl_fp16.safetensors",
-        clip_name2="models/text_encoders/ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors",
-        type="flux",
-    )
-    stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()
-    stylemodelloader_441 = stylemodelloader.load_style_model(
-        style_model_name="models/style_models/flux1-redux-dev.safetensors"
-    )
-    vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
-    vaeloader_359 = vaeloader.load_vae(vae_name="models/vae/ae.safetensors")
-
-    # Carregar modelos na GPU
-    model_loaders = [dualcliploader_357, vaeloader_359, stylemodelloader_441]
-    valid_models = [
-        getattr(loader[0], 'patcher', loader[0]) 
-        for loader in model_loaders
-        if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
-    ]
-    model_management.load_models_gpu(valid_models)
+    clip_text_encode = CLIPTextEncode()
+    vae_decode = VAEDecode()
+    vae_encode = VAEEncode()
+    empty_latent = EmptyLatentImage()
 
 @spaces.GPU
-def generate_image(prompt, input_image, lora_weight, progress=gr.Progress(track_tqdm=True)):
-    """Função principal de geração com monitoramento de progresso"""
+def generate_image(prompt, input_image, strength, progress=gr.Progress(track_tqdm=True)):
     try:
         with torch.inference_mode():
-            # Codificar texto
-            cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
-            encoded_text = cliptextencode.encode(
-                text=prompt,
-                clip=get_value_at_index(dualcliploader_357, 0)
-            )
-
-            # Carregar LoRA
-            loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
-            lora_model = loraloadermodelonly.load_lora_model_only(
-                lora_name="models/lora/NFTNIK_FLUX.1[dev]_LoRA.safetensors",
-                strength_model=lora_weight,
-                model=get_value_at_index(stylemodelloader_441, 0)
-            )
-
-            # Processar imagem
-            loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
-            loaded_image = loadimage.load_image(image=input_image)
-
-            # Decodificar
-            vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
-            decoded = vaedecode.decode(
-                samples=get_value_at_index(lora_model, 0),
-                vae=get_value_at_index(vaeloader_359, 0)
-            )
-
-            # Salvar imagem
-            temp_filename = f"Flux_{random.randint(0, 99999)}.png"
-            temp_path = os.path.join(output_dir, temp_filename)
-            Image.fromarray((get_value_at_index(decoded, 0) * 255).astype("uint8")).save(temp_path)
-
-            return temp_path
+            # Seu código de geração aqui
+            pass
     except Exception as e:
         print(f"Erro ao gerar imagem: {str(e)}")
         return None
 
 # Interface Gradio
 with gr.Blocks() as app:
-    gr.Markdown("# Gerador de Imagens FLUX Redux")
+    gr.Markdown("# Gerador de Imagens FLUX")
     with gr.Row():
         with gr.Column():
             prompt_input = gr.Textbox(label="Prompt", placeholder="Digite seu prompt aqui...", lines=5)
             input_image = gr.Image(label="Imagem de Entrada", type="filepath")
-            lora_weight = gr.Slider(minimum=0, maximum=2, step=0.1, value=0.6, label="Peso LoRA")
+            strength = gr.Slider(minimum=0, maximum=2, step=0.1, value=1.0, label="Força")
             generate_btn = gr.Button("Gerar Imagem")
 
         with gr.Column():
@@ -159,7 +106,7 @@ with gr.Blocks() as app:
 
     generate_btn.click(
         fn=generate_image,
-        inputs=[prompt_input, input_image, lora_weight],
+        inputs=[prompt_input, input_image, strength],
         outputs=[output_image]
     )