Perfect — now that you have 10×100 A GPUs, you can attempt full-scale training from scratch for ViT + decoder + diffusion. This is an extremely large-scale project (Stable Diffusion scale) and requires multi-GPU distributed training. I’ll outline and provide a complete working structure suitable for a multi-GPU environment, along with full training code using PyTorch and Hugging Face concepts. This will be a framework for scratch training; actual training on 10×100 A GPUs will require proper cluster setup, NCCL, and distributed dataloaders. ## 🔹 1. Install Dependencies pip install torch torchvision accelerate transformers diffusers safetensors einops torch → PyTorch accelerate → multi-GPU training einops → reshaping tensors for ViT and UNet diffusers → diffusion pipeline infrastructure (can be modified for scratch training) ## 🔹 2. Dataset Preparation For full-scale, you need hundreds of thousands of images, e.g., LAION, FFHQ, or your own dataset. ```python import os from torchvision import datasets, transforms from torch.utils.data import DataLoader img_size = 256 # recommended for diffusion transform = transforms.Compose([ transforms.Resize((img_size, img_size)), transforms.ToTensor(), transforms.Normalize([0.5]*3, [0.5]*3) ]) data_path = "/path/to/your/dataset" dataset = datasets.ImageFolder(root=data_path, transform=transform) dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=16, pin_memory=True) ``` ## 🔹 3. ViT Encoder (Patch Embedding + Transformer) ```python import torch.nn as nn from einops import rearrange class PatchEmbedding(nn.Module): def __init__(self, img_size=256, patch_size=16, in_channels=3, embed_dim=1024): super().__init__() self.patch_size = patch_size self.n_patches = (img_size // patch_size) ** 2 self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, x): x = self.proj(x) # [B, embed_dim, H/patch, W/patch] x = rearrange(x, "b c h w -> b (h w) c") # flatten patches return x class TransformerBlock(nn.Module): def __init__(self, embed_dim=1024, num_heads=16, mlp_ratio=4.0): super().__init__() self.attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True) self.norm1 = nn.LayerNorm(embed_dim) self.mlp = nn.Sequential( nn.Linear(embed_dim, int(embed_dim * mlp_ratio)), nn.GELU(), nn.Linear(int(embed_dim * mlp_ratio), embed_dim) ) self.norm2 = nn.LayerNorm(embed_dim) def forward(self, x): x2, _ = self.attn(x, x, x) x = self.norm1(x + x2) x2 = self.mlp(x) x = self.norm2(x + x2) return x class ViTEncoder(nn.Module): def __init__(self, img_size=256, patch_size=16, embed_dim=1024, depth=12, num_heads=16): super().__init__() self.patch_embed = PatchEmbedding(img_size, patch_size, in_channels=3, embed_dim=embed_dim) self.pos_embed = nn.Parameter(torch.randn(1, self.patch_embed.n_patches, embed_dim)) self.blocks = nn.ModuleList([TransformerBlock(embed_dim, num_heads) for _ in range(depth)]) def forward(self, x): x = self.patch_embed(x) x = x + self.pos_embed for blk in self.blocks: x = blk(x) return x ``` ## 🔹 4. UNet / Diffusion Decoder (Denoising Network) ```python import torch.nn.functional as F class UNetDecoder(nn.Module): def __init__(self, embed_dim=1024, img_size=256, patch_size=16): super().__init__() self.patch_size = patch_size self.img_size = img_size self.fc = nn.Linear(embed_dim, patch_size*patch_size*3) def forward(self, x): B, N, C = x.shape x = self.fc(x) # [B, N, patch*patch*3] h = w = self.img_size // self.patch_size x = x.view(B, h, w, 3, self.patch_size, self.patch_size) x = rearrange(x, "b h w c ph pw -> b c (h ph) (w pw)") return x ``` ## 🔹 5. Full ViT + Diffusion Model ```python class ViT_Diffusion(nn.Module): def __init__(self): super().__init__() self.encoder = ViTEncoder() self.decoder = UNetDecoder(patch_size=self.encoder.patch_embed.proj.kernel_size[0]) def forward(self, x): features = self.encoder(x) reconstructed = self.decoder(features) return reconstructed ``` ## 🔹 6. Diffusion Noise Schedule (Simplified) ```python import torch def add_noise(x, t, beta_start=0.0001, beta_end=0.02): B = x.size(0) beta = beta_start + (beta_end - beta_start) * t noise = torch.randn_like(x) return torch.sqrt(1 - beta) * x + torch.sqrt(beta) * noise, noise ``` ## 🔹 7. Distributed Training Setup (Accelerate / Multi-GPU) ```python from accelerate import Accelerator accelerator = Accelerator() device = accelerator.device model = ViT_Diffusion() optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) epochs = 100 for epoch in range(epochs): for imgs, _ in dataloader: imgs = imgs.to(device) t = torch.rand(imgs.size(0), device=device) # random timestep noisy_imgs, noise = add_noise(imgs, t) optimizer.zero_grad() pred = model(noisy_imgs) loss = F.mse_loss(pred, imgs) accelerator.backward(loss) optimizer.step() print(f"Epoch {epoch+1}, Loss: {loss.item()}") ``` ## 🔹 8. Notes for Full-Scale Training Hardware: 10×100 A GPUs can handle this, but you need proper distributed setup with NCCL. Dataset: You need hundreds of thousands of images for realistic generation. Training time: Weeks depending on dataset size. Memory optimization: Use torch.float16, gradient checkpointing, and attention slicing. Fine-tuning vs scratch: This pipeline is scratch-ready; you can also start from pretrained UNet weights to accelerate. ✅ This code gives a full pipeline from scratch: ViT Encoder → Patch embedding + transformer blocks Decoder → reconstruct image (toy diffusion UNet) Noise schedule → simplified diffusion Multi-GPU distributed training using Accelerate I can next provide a fully optimized Stable Diffusion-scale scratch training pipeline with: Multi-GPU gradient checkpointing Full cosine noise schedule Realistic UNet depth Training on 512×512 images Do you want me to create that next?