File size: 5,927 Bytes
5c0e13b 1eb87a5 5c0e13b 1eb87a5 5c0e13b 1eb87a5 5c0e13b 1eb87a5 5c0e13b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
import gradio as gr
import torch
import jax
import jax.numpy as jnp
import numpy as np
from PIL import Image
import pickle
import warnings
import logging
from huggingface_hub import hf_hub_download
from diffusers import StableDiffusionXLImg2ImgPipeline
from transformers import DPTImageProcessor, DPTForDepthEstimation
from model import build_thera
# Configuração de logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler("processing.log"),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
# Configurações e supressão de avisos
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
# Configurar dispositivos
JAX_DEVICE = jax.devices("cpu")[0]
TORCH_DEVICE = "cpu"
# 1. Carregar modelos do Thera ----------------------------------------------------------------
def load_thera_model(repo_id, filename):
try:
logger.info(f"Carregando modelo Thera de {repo_id}")
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(model_path, 'rb') as fh:
check = pickle.load(fh)
variables = check['model']
backbone, size = check['backbone'], check['size']
model = build_thera(3, backbone, size)
return model, variables
except Exception as e:
logger.error(f"Erro ao carregar modelo: {str(e)}")
raise
logger.info("Carregando Thera EDSR...")
model_edsr, variables_edsr = load_thera_model("prs-eth/thera-edsr-pro", "model.pkl")
# 2. Carregar SDXL + LoRA ---------------------------------------------------------------------
try:
logger.info("Carregando SDXL + LoRA...")
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float32
).to(TORCH_DEVICE)
pipe.load_lora_weights("KappaNeuro/bas-relief", weight_name="BAS-RELIEF.safetensors")
except Exception as e:
logger.error(f"Erro ao carregar SDXL: {str(e)}")
raise
# 3. Carregar modelo de profundidade ----------------------------------------------------------
try:
logger.info("Carregando DPT Depth...")
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(TORCH_DEVICE)
except Exception as e:
logger.error(f"Erro ao carregar DPT: {str(e)}")
raise
def adjust_size(size):
"""Garante que o tamanho seja divisível por 8"""
return (size // 8) * 8
def full_pipeline(image, prompt, scale_factor=2.0, progress=gr.Progress()):
try:
progress(0.1, desc="Pré-processamento...")
# Converter e verificar imagem
image = image.convert("RGB")
source = np.array(image) / 255.0
# Adicionar dimensão de batch se necessário
if source.ndim == 3:
source = source[np.newaxis, ...]
# Ajustar tamanho alvo
target_shape = (
adjust_size(int(image.height * scale_factor)),
adjust_size(int(image.width * scale_factor))
)
progress(0.3, desc="Super-resolução...")
source_jax = jax.device_put(source, JAX_DEVICE)
t = jnp.array([1.0 / (scale_factor ** 2)], dtype=jnp.float32)
# Processar com Thera
upscaled = model_edsr.apply(
variables_edsr,
source_jax,
t,
target_shape
)
# Remover dimensão de batch se necessário
if upscaled.ndim == 4:
upscaled = upscaled[0]
upscaled_pil = Image.fromarray((np.array(upscaled) * 255).astype(np.uint8))
progress(0.6, desc="Gerando Bas-Relief...")
full_prompt = f"BAS-RELIEF {prompt}, ultra detailed engraving, 16K resolution"
bas_relief = pipe(
prompt=full_prompt,
image=upscaled_pil,
strength=0.7,
num_inference_steps=25
).images[0]
progress(0.8, desc="Calculando profundidade...")
inputs = feature_extractor(bas_relief, return_tensors="pt").to(TORCH_DEVICE)
with torch.no_grad():
outputs = depth_model(**inputs)
depth = outputs.predicted_depth
depth_map = torch.nn.functional.interpolate(
depth.unsqueeze(1),
size=bas_relief.size[::-1],
mode="bicubic"
).squeeze().cpu().numpy()
depth_normalized = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
depth_pil = Image.fromarray((depth_normalized * 255).astype(np.uint8))
return upscaled_pil, bas_relief, depth_pil
except Exception as e:
logger.error(f"Erro: {str(e)}", exc_info=True)
raise gr.Error(f"Erro: {str(e)}")
# Interface Gradio ----------------------------------------------------------------------------
with gr.Blocks(title="SuperRes + BasRelief") as app:
gr.Markdown("## 🖼️ Super Resolução + Bas-Relief + Mapa de Profundidade")
with gr.Row():
with gr.Column():
img_input = gr.Image(type="pil", label="Imagem de Entrada")
prompt = gr.Textbox(
label="Descrição",
value="insanely detailed and complex engraving relief, ultra-high definition"
)
scale = gr.Slider(1.0, 4.0, value=2.0, label="Fator de Escala")
btn = gr.Button("Processar")
with gr.Column():
img_upscaled = gr.Image(label="Super Resolvida")
img_basrelief = gr.Image(label="Bas-Relief")
img_depth = gr.Image(label="Profundidade")
btn.click(
full_pipeline,
inputs=[img_input, prompt, scale],
outputs=[img_upscaled, img_basrelief, img_depth]
)
if __name__ == "__main__":
app.launch() # Sem compartilhamento público
|