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# dreamo_helpers.py (CORRIGIDO)
# Módulo de serviço para o DreamO, com gestão de memória e aceitando uma lista dinâmica de referências.
import os
import cv2
import torch
import numpy as np
from PIL import Image
import huggingface_hub
import gc
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from torchvision.transforms.functional import normalize
from dreamo.dreamo_pipeline import DreamOPipeline
from dreamo.utils import img2tensor, tensor2img
from tools import BEN2
class Generator:
def __init__(self):
self.cpu_device = torch.device('cpu')
self.gpu_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Carregando modelos DreamO para a CPU...")
model_root = 'black-forest-labs/FLUX.1-dev'
self.dreamo_pipeline = DreamOPipeline.from_pretrained(model_root, torch_dtype=torch.bfloat16)
self.dreamo_pipeline.load_dreamo_model(self.cpu_device, use_turbo=True)
self.bg_rm_model = BEN2.BEN_Base().to(self.cpu_device).eval()
huggingface_hub.hf_hub_download(repo_id='PramaLLC/BEN2', filename='BEN2_Base.pth', local_dir='models')
self.bg_rm_model.loadcheckpoints('models/BEN2_Base.pth')
self.face_helper = FaceRestoreHelper(
upscale_factor=1, face_size=512, crop_ratio=(1, 1),
det_model='retinaface_resnet50', save_ext='png', device=self.cpu_device,
)
print("Modelos DreamO prontos (na CPU).")
def to_gpu(self):
if self.gpu_device.type == 'cpu': return
print("Movendo modelos DreamO para a GPU...")
self.dreamo_pipeline.to(self.gpu_device)
self.bg_rm_model.to(self.gpu_device)
self.face_helper.device = self.gpu_device
self.dreamo_pipeline.t5_embedding.to(self.gpu_device)
self.dreamo_pipeline.task_embedding.to(self.gpu_device)
self.dreamo_pipeline.idx_embedding.to(self.gpu_device)
if hasattr(self.face_helper, 'face_parse'): self.face_helper.face_parse.to(self.gpu_device)
if hasattr(self.face_helper, 'face_det'): self.face_helper.face_det.to(self.gpu_device)
print("Modelos DreamO na GPU.")
def to_cpu(self):
if self.gpu_device.type == 'cpu': return
print("Descarregando modelos DreamO da GPU...")
self.dreamo_pipeline.to(self.cpu_device)
self.bg_rm_model.to(self.cpu_device)
self.face_helper.device = self.cpu_device
self.dreamo_pipeline.t5_embedding.to(self.cpu_device)
self.dreamo_pipeline.task_embedding.to(self.cpu_device)
self.dreamo_pipeline.idx_embedding.to(self.cpu_device)
if hasattr(self.face_helper, 'face_det'): self.face_helper.face_det.to(self.cpu_device)
if hasattr(self.face_helper, 'face_parse'): self.face_helper.face_parse.to(self.cpu_device)
gc.collect()
if torch.cuda.is_available(): torch.cuda.empty_cache()
@torch.inference_mode()
def generate_image_with_gpu_management(self, reference_items, prompt, width, height):
try:
self.to_gpu()
ref_conds = []
for idx, item in enumerate(reference_items):
ref_image_np = item.get('image_np')
ref_task = item.get('task')
if ref_image_np is not None:
if ref_task == "id":
ref_image = self.get_align_face(ref_image_np)
elif ref_task != "style":
ref_image = self.bg_rm_model.inference(Image.fromarray(ref_image_np))
else:
ref_image = ref_image_np
ref_image_tensor = img2tensor(np.array(ref_image), bgr2rgb=False).unsqueeze(0) / 255.0
ref_image_tensor = (2 * ref_image_tensor - 1.0).to(self.gpu_device, dtype=torch.bfloat16)
ref_conds.append({'img': ref_image_tensor, 'task': ref_task, 'idx': idx + 1})
# <<< CORREÇÃO APLICADA AQUI >>>
image = self.dreamo_pipeline(
prompt=prompt,
width=width,
height=height,
num_inference_steps=12,
guidance_scale=4.5,
ref_conds=ref_conds,
generator=torch.Generator(device=self.gpu_device).manual_seed(42) # Usar o dispositivo GPU
).images[0]
return image
finally:
self.to_cpu()
@torch.no_grad()
def get_align_face(self, img):
self.face_helper.clean_all()
image_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
self.face_helper.read_image(image_bgr)
self.face_helper.get_face_landmarks_5(only_center_face=True)
self.face_helper.align_warp_face()
if len(self.face_helper.cropped_faces) == 0: return None
align_face = self.face_helper.cropped_faces[0]
input_tensor = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0
input_tensor = input_tensor.to(self.gpu_device) # Necessário para o face_parse
parsing_out = self.face_helper.face_parse(normalize(input_tensor, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
parsing_out = parsing_out.argmax(dim=1, keepdim=True)
bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
bg = sum(parsing_out == i for i in bg_label).bool()
white_image = torch.ones_like(input_tensor)
face_features_image = torch.where(bg, white_image, input_tensor)
return tensor2img(face_features_image, rgb2bgr=False)
# --- Instância Singleton ---
# A inicialização permanece a mesma, pois é condicional dentro do app.py principal
print("Inicializando o Pintor de Cenas (DreamO Helper)...")
hf_token = os.getenv('HF_TOKEN')
if hf_token: huggingface_hub.login(token=hf_token)
dreamo_generator_singleton = Generator()
print("Pintor de Cenas (DreamO Helper) pronto.") |