app.py

import os
import sys
import random
from typing import Sequence, Mapping, Any, Union

import torch
import gradio as gr
from PIL import Image
from huggingface_hub import hf_hub_download

import spaces  # Se estiver no Hugging Face Spaces. Se não, pode remover.

#####################################
# 1. Funções auxiliares de caminho e import
#####################################

def find_path(name: str, path: str = None) -> str:
    """Busca recursivamente por uma pasta/arquivo 'name' a partir de 'path'."""
    if path is None:
        path = os.getcwd()
    if name in os.listdir(path):
        path_name = os.path.join(path, name)
        print(f"{name} encontrado em: {path_name}")
        return path_name
    parent_directory = os.path.dirname(path)
    if parent_directory == path:
        return None
    return find_path(name, parent_directory)

def add_comfyui_directory_to_sys_path() -> None:
    """Adiciona o diretório ComfyUI ao sys.path, caso encontrado."""
    comfyui_path = find_path("ComfyUI")
    if comfyui_path is not None and os.path.isdir(comfyui_path):
        sys.path.append(comfyui_path)
        print(f"Diretório ComfyUI adicionado ao sys.path: {comfyui_path}")
    else:
        print("Não foi possível encontrar o diretório ComfyUI.")

def add_extra_model_paths() -> None:
    """
    Carrega configurações extras de caminhos de modelos, se existir 
    um arquivo 'extra_model_paths.yaml'.
    """
    try:
        from main import load_extra_path_config
    except ImportError:
        # Dependendo da versão do ComfyUI, pode estar em 'utils.extra_config'
        from utils.extra_config import load_extra_path_config

    extra_model_paths = find_path("extra_model_paths.yaml")
    if extra_model_paths is not None:
        load_extra_path_config(extra_model_paths)
    else:
        print("Arquivo extra_model_paths.yaml não foi encontrado.")

def import_custom_nodes() -> None:
    """
    Executa a inicialização de nós extras e o servidor do ComfyUI (caso necessário),
    similar ao que ocorre no segundo script.
    """
    import asyncio
    import execution
    from nodes import init_extra_nodes
    import server

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    server_instance = server.PromptServer(loop)
    execution.PromptQueue(server_instance)
    init_extra_nodes()

#####################################
# 2. Ajustando o ambiente ComfyUI
#####################################

add_comfyui_directory_to_sys_path()
add_extra_model_paths()
import_custom_nodes()

#####################################
# 3. Importando nós do ComfyUI
#####################################
from comfy import model_management
from nodes import (
    NODE_CLASS_MAPPINGS,
    DualCLIPLoader,
    CLIPVisionLoader,
    StyleModelLoader,
    VAELoader,
    CLIPTextEncode,
    LoadImage,
    EmptyLatentImage,
    VAEDecode
)

#####################################
# 4. Download de modelos (ajuste conforme sua necessidade)
#####################################

# Exemplo de downloads (ajuste conforme seus modelos):
os.makedirs("models/text_encoders", exist_ok=True)
os.makedirs("models/style_models", exist_ok=True)
os.makedirs("models/diffusion_models", exist_ok=True)
os.makedirs("models/vae", exist_ok=True)
os.makedirs("models/clip_vision", exist_ok=True)

try:
    print("Baixando modelo Style (flux1-redux-dev.safetensors)...")
    hf_hub_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", 
                    filename="flux1-redux-dev.safetensors",
                    local_dir="models/style_models")
    print("Baixando T5 (t5xxl_fp16.safetensors)...")
    hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", 
                    filename="t5xxl_fp16.safetensors",
                    local_dir="models/text_encoders")

    print("Baixando CLIP L (ViT-L-14) ...")
    hf_hub_download(repo_id="zer0int/CLIP-GmP-ViT-L-14",
                    filename="ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors",
                    local_dir="models/text_encoders")
    print("Baixando VAE (ae.safetensors)...")
    hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev",
                    filename="ae.safetensors",
                    local_dir="models/vae")
    print("Baixando flux1-dev.safetensors (modelo difusão)...")
    hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev",
                    filename="flux1-dev.safetensors",
                    local_dir="models/diffusion_models")
    print("Baixando CLIP Vision (model.safetensors)...")
    hf_hub_download(repo_id="google/siglip-so400m-patch14-384",
                    filename="model.safetensors",
                    local_dir="models/clip_vision")
except Exception as e:
    print("Algum download falhou:", e)

#####################################
# 5. Carregar modelos via ComfyUI
#####################################

# Carregando CLIP (DualCLIPLoader)
dualcliploader = DualCLIPLoader()
clip_model = dualcliploader.load_clip(
    clip_name1="t5xxl_fp16.safetensors",
    clip_name2="ViT-L-14-TEXT-detail-improved-hiT-GmP-HF.safetensors",
    type="flux"
)

# Carregando CLIP Vision
clipvisionloader = CLIPVisionLoader()
clip_vision_model = clipvisionloader.load_clip(
    clip_name="model.safetensors"
)

# Carregando Style Model
stylemodelloader = StyleModelLoader()
style_model = stylemodelloader.load_style_model(
    style_model_name="flux1-redux-dev.safetensors"
)

# Carregando VAE
vaeloader = VAELoader()
vae_model = vaeloader.load_vae(
    vae_name="ae.safetensors"
)

# (Opcional) Se tiver um model UNet, faça UNETLoader, etc.

# Opcional: Carregar para GPU
model_management.load_models_gpu([
    loader[0] for loader in [clip_model, clip_vision_model, style_model, vae_model]
])

#####################################
# 6. Funções auxiliares e placeholders
#####################################

def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
    """Retorna o 'index' de um objeto que pode ser um dict ou lista."""
    try:
        return obj[index]
    except KeyError:
        return obj["result"][index]

#####################################
# 7. Definir workflow simplificado
#####################################

@spaces.GPU  # Se estiver no Hugging Face Spaces. Senão, remova.
def generate_image(
    prompt: str,
    input_image_path: str,
    lora_weight: float,
    guidance: float,
    downsampling_factor: float,
    weight: float,
    seed: int,
    width: int,
    height: int,
    batch_size: int,
    steps: int,
    progress=gr.Progress(track_tqdm=True)
):
    """
    Gera imagem usando um fluxo simplificado, similar ao primeiro script.
    """
    try:
        # Garantindo repetibilidade do seed
        torch.manual_seed(seed)
        random.seed(seed)

        # 1) Encode Texto
        cliptextencode = CLIPTextEncode()
        encoded_text = cliptextencode.encode(
            text=prompt,
            clip=get_value_at_index(clip_model, 0)
        )

        # 2) Carregar imagem de entrada
        loadimage = LoadImage()
        loaded_image = loadimage.load_image(image=input_image_path)

        # 3) Flux Guidance (se existir)
        fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
        flux_guided = fluxguidance.append(
            guidance=guidance,
            conditioning=get_value_at_index(encoded_text, 0)
        )

        # 4) Redux Advanced (aplicar style model)
        reduxadvanced = NODE_CLASS_MAPPINGS["ReduxAdvanced"]()
        redux_result = reduxadvanced.apply_stylemodel(
            downsampling_factor=downsampling_factor,
            downsampling_function="area",
            mode="keep aspect ratio",
            weight=weight,
            conditioning=get_value_at_index(flux_guided, 0),
            style_model=get_value_at_index(style_model, 0),
            clip_vision=get_value_at_index(clip_vision_model, 0),
            image=get_value_at_index(loaded_image, 0)
        )

        # 5) Empty Latent
        emptylatent = EmptyLatentImage()
        empty_latent = emptylatent.generate(
            width=width,
            height=height,
            batch_size=batch_size
        )

        # 6) KSampler (no ComfyUI atual, há "KSamplerSelect" ou "KSampler")
        ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
        sampled = ksampler.sample(
            seed=seed,
            steps=steps,
            cfg=1,  # Exemplo de CFG = 1
            sampler_name="euler",
            scheduler="simple",
            denoise=1,
            model=get_value_at_index(style_model, 0),     # Usa o style model como UNet? (depende da config)
            positive=get_value_at_index(redux_result, 0),
            negative=get_value_at_index(flux_guided, 0),
            latent_image=get_value_at_index(empty_latent, 0)
        )

        # 7) Decodificar VAE
        vaedecode = VAEDecode()
        decoded = vaedecode.decode(
            samples=get_value_at_index(sampled, 0),
            vae=get_value_at_index(vae_model, 0)
        )

        # 8) Salvar imagem
        output_dir = "output"
        os.makedirs(output_dir, exist_ok=True)
        temp_filename = f"Flux_{random.randint(0, 99999)}.png"
        temp_path = os.path.join(output_dir, temp_filename)

        # No ComfyUI, 'decoded[0]' pode ser um tensor [C,H,W] normalizado
        # ou algo no formato [N,C,H,W]. Precisamos converter para PIL:
        # Se for um batch, pegue o primeiro item. Ajuste se quiser batch maior.
        image_data = get_value_at_index(decoded, 0)
        # Normalmente, se for "float [0,1]" em C,H,W:
        # Precisamos mover pro CPU e converter em numpy
        if isinstance(image_data, torch.Tensor):
            image_data = image_data.cpu().numpy()

        # Se a imagem estiver em [C,H,W], transpor para [H,W,C] e escalar 0..255
        if len(image_data.shape) == 3:
            image_data = image_data.transpose(1, 2, 0)
        image_data = (image_data * 255).clip(0, 255).astype("uint8")

        pil_image = Image.fromarray(image_data)
        pil_image.save(temp_path)

        return temp_path
    except Exception as e:
        print(f"Erro ao gerar imagem: {str(e)}")
        return None

#####################################
# 8. Interface Gradio (similar ao primeiro snippet)
#####################################

with gr.Blocks() as app:
    gr.Markdown("# FLUX Redux Image Generator (Simplificado)")

    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(
                label="Prompt",
                placeholder="Escreva seu prompt...",
                lines=5
            )
            input_image = gr.Image(
                label="Imagem de Entrada",
                type="filepath"
            )

            with gr.Row():
                with gr.Column():
                    lora_weight = gr.Slider(
                        minimum=0,
                        maximum=2,
                        step=0.1,
                        value=0.6,
                        label="LoRA Weight (não usado nesse fluxo)"
                    )
                    guidance = gr.Slider(
                        minimum=0,
                        maximum=20,
                        step=0.1,
                        value=3.5,
                        label="Guidance"
                    )
                    downsampling_factor = gr.Slider(
                        minimum=1,
                        maximum=8,
                        step=1,
                        value=3,
                        label="Downsampling Factor"
                    )
                    weight = gr.Slider(
                        minimum=0,
                        maximum=2,
                        step=0.1,
                        value=1.0,
                        label="Redux Model Weight"
                    )
                with gr.Column():
                    seed = gr.Number(
                        value=random.randint(1, 2**64),
                        label="Seed",
                        precision=0
                    )
                    width = gr.Number(
                        value=512,
                        label="Width",
                        precision=0
                    )
                    height = gr.Number(
                        value=512,
                        label="Height",
                        precision=0
                    )
                    batch_size = gr.Number(
                        value=1,
                        label="Batch Size",
                        precision=0
                    )
                    steps = gr.Number(
                        value=20,
                        label="Steps",
                        precision=0
                    )

            generate_btn = gr.Button("Generate Image")

        with gr.Column():
            output_image = gr.Image(label="Generated Image", type="filepath")

    generate_btn.click(
        fn=generate_image,
        inputs=[
            prompt_input,
            input_image,
            lora_weight,
            guidance,
            downsampling_factor,
            weight,
            seed,
            width,
            height,
            batch_size,
            steps
        ],
        outputs=[output_image]
    )

if __name__ == "__main__":
    # Você pode usar app.launch(share=True) se quiser compartilhar via link.
    app.launch()