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c8c00f0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | import os
import argparse
import torch
import numpy as np
from torchvision.utils import save_image
from diffusers.models import AutoencoderKL
import clip.clip as clip
from models_add_cross_concate import DiT
from diffusion import create_diffusion
from autoencoder import *
# Enable TF32 for fast execution on modern NVIDIA GPUs
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def rgb_to_gray(tensor):
r, g, b = tensor[:, 0], tensor[:, 1], tensor[:, 2]
gray = 0.299 * r + 0.587 * g + 0.114 * b
return gray
def iterative_thresholding_batch(gray_tensor):
gray_np = gray_tensor.detach().cpu().numpy()
binarized = np.zeros_like(gray_np, dtype=np.uint8)
for i in range(gray_np.shape[0]):
img = gray_np[i]
T = img.mean()
prev_T = -1
while abs(T - prev_T) > 1e-4:
prev_T = T
G1 = img[img >= T]
G2 = img[img < T]
m1 = G1.mean() if G1.size > 0 else 0
m2 = G2.mean() if G2.size > 0 else 0
T = (m1 + m2) / 2
binarized[i] = (img >= T).astype(np.uint8)
return torch.from_numpy(binarized).to(gray_tensor.device)
def binarize_tensor_iterative(x):
gray = rgb_to_gray(x)
binary = iterative_thresholding_batch(gray)
return binary.unsqueeze(1)
def get_label(data_path):
"""Safely extracts defect labels from 4-level dataset hierarchy."""
label_list1 = []
if not os.path.exists(data_path):
return label_list1
for name_class in os.listdir(data_path):
img_dir = os.path.join(data_path, name_class, 'img')
if os.path.exists(img_dir) and os.path.isdir(img_dir):
for class_object in os.listdir(img_dir):
defect_dir = os.path.join(img_dir, class_object)
if os.path.isdir(defect_dir) and class_object != 'good':
label_list1.append(f"{class_object} {name_class}")
return label_list1
def gen(args):
data_path = args.data
label_list = get_label(data_path)
if not label_list:
print(f"❌ No valid defect subfolders found in {data_path}. Please check directory structure.")
return
print(f"📋 Found defect categories to generate: {label_list}")
image_size = args.imagesize
device = "cuda" if torch.cuda.is_available() else "cpu"
latent_size = image_size // 8
# 1. Load CLIP model
model_clip, _ = clip.load('RN50', device)
# 2. Setup DiT architecture and weights
model = DiT(
depth=28, hidden_size=1152, patch_size=2,
num_heads=16, input_size=latent_size, num_classes=1000
).to(device)
print(f"📦 Loading checkpoint from: {args.ckpt}")
checkpoint = torch.load(args.ckpt, map_location=device)
if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
model.load_state_dict(checkpoint['model_state_dict'])
else:
model.load_state_dict(checkpoint)
model.eval()
# 3. Setup VAE and Diffusion pipeline
diffusion = create_diffusion(timestep_respacing="50")
vae = AutoencoderKL.from_pretrained(args.vae).to(device)
os.makedirs(args.out_dir, exist_ok=True)
num_img = args.batchsize
# 4. Generate specified number of output batches (replaces infinite while loop)
for sample_round in range(args.num_samples):
print(f"\n🔄 --- Generating Batch {sample_round + 1}/{args.num_samples} ---")
for c in label_list:
defect_name, class_name = c.split()[0], c.split()[1]
print(f"🎨 Generating defect: '{defect_name}' on object: '{class_name}'...")
# Prepare text embeddings for dual-branch CFG
y_null_product = torch.cat([clip.tokenize("a photo of good industry")] * num_img).to(device)
y_null_good = torch.cat([clip.tokenize(f"a photo of good {class_name}")] * num_img).to(device)
with torch.no_grad():
y_null_product = model_clip.encode_text(y_null_product)
y_null_good = model_clip.encode_text(y_null_good)
y_null_product = (y_null_product / y_null_product.norm(dim=-1, keepdim=True)).float()
y_null_good = (y_null_good / y_null_good.norm(dim=-1, keepdim=True)).float()
only_good = torch.cat([clip.tokenize("a photo of good")] * num_img).to(device)
defect = torch.cat([clip.tokenize(f"a photo of {defect_name}")] * num_img).to(device)
classes = torch.cat([clip.tokenize(f"a photo of {class_name}")] * num_img).to(device)
classes_industry = torch.cat([clip.tokenize("a photo of industry")] * num_img).to(device)
y_all = torch.cat([clip.tokenize(f"a photo of {c}")] * num_img).to(device)
with torch.no_grad():
only_good = model_clip.encode_text(only_good)
defect = model_clip.encode_text(defect)
classes = model_clip.encode_text(classes)
classes_industry = model_clip.encode_text(classes_industry)
y_all = model_clip.encode_text(y_all)
only_good = (only_good / only_good.norm(dim=-1, keepdim=True)).float()
defect = (defect / defect.norm(dim=-1, keepdim=True)).float()
classes_industry = (classes_industry / classes_industry.norm(dim=-1, keepdim=True)).float()
classes = (classes / classes.norm(dim=-1, keepdim=True)).float()
y_all = (y_all / y_all.norm(dim=-1, keepdim=True)).float()
y_defect_class = [defect, classes, y_all]
y_good_class = [only_good, classes, y_null_good]
z = torch.randn(num_img, 4, latent_size, latent_size, device=device)
z = torch.cat([z, z], 0)
y = [y_defect_class, y_good_class]
for num in np.arange(0.5, 3.0, 0.5):
model_kwargs = dict(y=y, cfg_scale=float(num))
with torch.no_grad():
samples, cross = diffusion.p_sample_loop(
model.forward_with_cfg_2,
z.shape,
z,
clip_denoised=False,
model_kwargs=model_kwargs,
progress=False,
device=device
)
img_gen, _ = samples.chunk(2, dim=0)
mask_gen, _ = cross.chunk(2, dim=0)
with torch.no_grad():
img_gen = vae.decode(img_gen / 0.18215).sample
mask_gen = vae.decode(mask_gen / 0.18215).sample
# Save generated images and binarized masks
img_path = os.path.join(args.out_dir, f"{class_name}_{defect_name}_cfg{num:.1f}_b{sample_round}.png")
mask_path = os.path.join(args.out_dir, f"{class_name}_{defect_name}_cfg{num:.1f}_b{sample_round}_mask.png")
save_image(img_gen, img_path, nrow=2, normalize=True)
mask_gen = binarize_tensor_iterative(mask_gen)
mask_gen = (mask_gen * 255).to(torch.uint8).float() / 255.0
save_image(mask_gen, mask_path, nrow=2, normalize=True)
print(f"\n✨ Generation complete! Synthetic pairs saved to: {os.path.abspath(args.out_dir)}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batchsize", type=int, default=2)
parser.add_argument("--num_samples", type=int, default=1, help="Number of sampling passes to run.")
parser.add_argument("--data", type=str, required=True)
parser.add_argument("--imagesize", type=int, choices=[256, 512], default=512)
parser.add_argument("--ckpt", type=str, required=True, help="Path to fine-tuned checkpoint.")
parser.add_argument("--vae", type=str, required=True, help="Path to VAE checkpoint.")
parser.add_argument("--out_dir", type=str, default="./generated_results", help="Directory to save generated samples.")
args = parser.parse_args()
gen(args) |