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README.md

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+---
+license: mit
+tags:
+  - pytorch
+  - gan
+  - dcgan
+  - image-generation
+  - face-generation
+  - celeba
+datasets:
+  - huggan/celeba
+---
+
+# DCGAN — Human Face Generation (CelebA)
+
+A Deep Convolutional GAN trained on CelebA to generate 64×64 human face images.
+
+## Config
+
+| Property | Value |
+|----------|-------|
+| Architecture | DCGAN (Radford et al., 2015) |
+| Dataset | CelebA — train split (162,770 images) |
+| Resolution | 64×64 RGB |
+| Latent dim | 100 |
+| Epochs | 25 |
+| Batch size | 256 |
+| Optimizer | Adam lr=0.0002 β₁=0.5 |
+| Mixed precision | torch.bfloat16 |
+
+## Usage
+
+```python
+import torch, torch.nn as nn
+from huggingface_hub import hf_hub_download
+
+class Generator(nn.Module):
+    def __init__(self, nz=100, ngf=64, nc=3):
+        super().__init__()
+        self.main = nn.Sequential(
+            nn.ConvTranspose2d(nz,    ngf*8, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf*8), nn.ReLU(True),
+            nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf*4), nn.ReLU(True),
+            nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf*2), nn.ReLU(True),
+            nn.ConvTranspose2d(ngf*2, ngf,   4, 2, 1, bias=False), nn.BatchNorm2d(ngf),   nn.ReLU(True),
+            nn.ConvTranspose2d(ngf,   nc,    4, 2, 1, bias=False), nn.Tanh()
+        )
+    def forward(self, x): return self.main(x)
+
+weights = hf_hub_download(repo_id="arzumanabbasov/DCGAN_CELEBA", filename="generator.pt")
+netG = Generator()
+netG.load_state_dict(torch.load(weights, map_location="cpu"))
+netG.eval()
+
+with torch.no_grad():
+    noise = torch.randn(16, 100, 1, 1)
+    faces = netG(noise)       # (16, 3, 64, 64) in [-1, 1]
+    faces = (faces + 1) / 2  # → [0, 1] for display
+```