Diffusion CIFAR10
A Denoising Diffusion Probabilistic Model (DDPM) trained on CIFAR-10 for unconditional image generation. The model generates 32x32 RGB images spanning all 10 CIFAR-10 categories without class conditioning.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 317 MB
- Versão
- vCkpt 199
- Atualizado
- 9 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
Diffusion-CIFAR10 — Unconditional DDPM on CIFAR-10
A Denoising Diffusion Probabilistic Model (DDPM) trained on CIFAR-10 for unconditional image generation. The model generates 32x32 RGB images spanning all 10 CIFAR-10 categories without class conditioning.
Model Description
Implements DDPM (Ho et al., 2020) with a U-Net denoising network featuring self-attention blocks and sinusoidal time embeddings.
Architecture — U-Net
Time Embedding:
- Sinusoidal timestep encoding → two-layer FFN with Swish activation
- Injected as an additive bias into every residual block
Residual Blocks (ResBlock):
- Two
3x3 Conv2dwithGroupNormand Swish activation - Time embedding added between convolutions
1x1projection on skip path when input/output channels differ
Attention (AttnBlock):
- Single-head self-attention (query, key, value projections)
- Applied at the bottleneck and selected resolution levels
Downsampling: Strided Conv2d (stride 2)
Upsampling: Nearest-neighbour interpolation + Conv2d
Diffusion Process
- Parameterization: Noise prediction (epsilon)
- Schedule: Linear beta schedule
- Loss: MSE between predicted and actual noise
Training Details
| Parameter | Value |
|---|---|
| Dataset | CIFAR-10 (50,000 images, 32x32 RGB) |
| Epochs | 200 (checkpoint: ckpt_199.pth) |
| Optimizer | AdamW, weight_decay=1e-4 |
| LR Schedule | Cosine annealing + linear warmup (GradualWarmupScheduler) |
| Gradient clipping | Enabled |
| Data augmentation | RandomHorizontalFlip, Normalize(0.5, 0.5, 0.5) |
| Workers | 4 |
Repository Contents
| File | Description |
|---|---|
| model.py | U-Net architecture (ResBlock, AttnBlock, DownSample, UpSample, TimeEmbedding) |
| diffusion.py | GaussianDiffusionTrainer + GaussianDiffusionSampler |
| train.py | Training loop |
| scheduler.py | GradualWarmupScheduler |
| main.py | Entry point |
| Checkpoints/ckpt_199.pth | Final model checkpoint |
| SampledImgs/ | Generated sample images |
How to Use
import torch
from model import UNet
from diffusion import GaussianDiffusionSampler
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UNet(T=1000, ch=128, ch_mult=[1,2,2,2], attn=[1], num_res_blocks=2, dropout=0.1)
model.load_state_dict(torch.load("Checkpoints/ckpt_199.pth", map_location=device))
model.eval().to(device)
sampler = GaussianDiffusionSampler(beta_1=1e-4, beta_t=0.02, model=model, T=1000).to(device)
with torch.no_grad():
x_T = torch.randn(16, 3, 32, 32, device=device)
samples = sampler(x_T)
samples = (samples.clamp(-1, 1) + 1) / 2 # rescale to [0, 1]
References
- Ho et al. (2020). Denoising Diffusion Probabilistic Models
License
MIT
Fontes
1 fonte
Fontes
1 fonteVerificado 9 de out.
Artefatos de modelo
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Artefatos de modelo
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Trechos de fonte
2 trechos--- license: mit tags: - diffusion - ddpm - unconditional-image-generation - pytorch - cifar10 - image-generation pipeline_tag: unconditional-image-generation datasets: - cifar10 --- # Diffusion-CIFAR10 — Unconditional DDPM on CIFAR-10 A Denoising Diffusion Probabilistic Model (DDPM) trained on CIFAR-10 for unconditional image generation. The model generates 32x32 RGB images spanning all 10 CIFAR-10 categories without class conditioning. ## Model Description Implements DDPM (Ho et al., 2020) with a U-Net denoising network featuring self-attention blocks and sinusoidal time embeddings. ### Architecture — U-Net **Time Embedding:** - Sinusoidal timestep encoding → two-layer FFN with Swish activation - Injected as an additive bias into every residual block **Residual Blocks (`ResBlock`):** - Two `3x3 Conv2d` with `GroupNorm` and Swish activation - Time embedding added between convolutions - `1x1` projection on skip path when input/output channels differ **Attention (`AttnBlock`):** - Single-head self-attention (query, key, value projections) - Applied at the bottleneck and selected resolution levels **Downsampling:** Strided `Conv2d` (stride 2) **Upsampling:** Nearest-neighbour interpolation + `Conv2d` ### Diffusion Process - **Parameterization:** Noise prediction (epsilon) - **Schedule:** Linear beta schedule - **Loss:** MSE between predicted and actual noise ## Training Details | Parameter | Value | |---|---| | Dataset | CIFAR-10 (50,000 images, 32x32 RGB) | | Epochs | 200 (checkpoint: ckpt_199.pth) | | Optimizer | AdamW, weight_decay=1e-4 | | LR Schedule | Cosine annealing + linear warmup (GradualWarmupScheduler) | | Gradient clipping | Enabled | | Data augmentation | RandomHorizontalFlip, Normalize(0.5, 0.5, 0.5) | | Workers | 4 | ## Repository Contents | File | Description | |---|---| | model.py | U-Net architecture (ResBlock, AttnBlock, DownSample, UpSample, TimeEmbedding) | | diffusion.py | GaussianDiffusionTrainer + GaussianDiffusionSampler | | train.py | Training loop | | scheduler.py | GradualWarmupScheduler | | main.py | Entry point | | Checkpoints/ckpt_199.pth | Final model checkpoint | | SampledImgs/ | Generated sample images | ## How to Use ```python import torch from model import UNet from diffusion import GaussianDiffusionSampler device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = UNet(T=1000, ch=12...
Source context: 0 downloads · 0 likes · Pipeline unconditional-image-generation · Repo YashNagraj75/Diffusion-CIFAR10
