Data & ML · Pro
Text-to-Image Diffusion Model From Scratch
Implement a latent diffusion model with a U-Net denoiser and CLIP conditioning, train it on an image set, and sample new images from a text prompt
You build a latent diffusion model from the ground up: a VAE compresses images into a compact latent space, a U-Net with cross-attention learns to reverse a noise process conditioned on CLIP text embeddings, and DDPM or DDIM samplers generate novel images from a prompt at inference time. The project covers the full training loop from forward noising to denoising loss, giving you genuine ownership of the architecture that powers Stable Diffusion. Building it yourself is the fastest way to understand why classifier-free guidance, noise schedules, and latent compression each matter. The result is a runnable text-to-image pipeline you can retrain on any image dataset you choose.
What you build
- Encode images into a compact latent space with a pretrained or from-scratch VAE
- Add noise to latents according to a configurable DDPM schedule (linear or cosine)
- Predict and remove noise with a U-Net whose decoder cross-attends to CLIP text embeddings
- Train on a small labeled image dataset with the standard epsilon-prediction MSE loss
- Sample new images using DDPM (full 1000-step) or the accelerated DDIM scheduler
- Apply classifier-free guidance at inference to steer output toward a text prompt
- Visualize the denoising trajectory frame-by-frame for a chosen prompt
What it teaches
- How the forward and reverse diffusion processes relate mathematically and why the epsilon-prediction objective works
- U-Net architecture with time-step conditioning and cross-attention for multimodal inputs
- The role of the VAE latent space in making diffusion tractable at high resolution
- CLIP text encoding and how cross-attention injects semantic guidance into the denoiser
- Classifier-free guidance and how the guidance scale trades diversity for prompt fidelity
- Accelerated sampling with DDIM and how it reduces 1000 steps to 20-50 without retraining
How it works
- 1
Text Prompt
- Raw string
↓ encode
- 2
CLIP Encoder
- 77-token embeddings
- null embedding (CFG)
↓ condition
- 3
Noisy Latent
- VAE encoder
- DDPM scheduler
- timestep t
↓ denoise
- 4
U-Net Denoiser
- Residual blocks
- Cross-attention
- Time embedding
↓ iterate
- 5
DDIM Sampler
- 50 steps
- Guidance scale
↓ decode
- 6
VAE Decoder
- 512x512 image
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Take it further
- Add LoRA fine-tuning adapters to condition on a new style or character without full retraining
- Implement SDXL-style multi-scale U-Net conditioning and compare sample quality at the same step budget
- Build a small Gradio UI that streams the denoising frames as an animation in the browser
