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Explore MoreCombining GAN and Diffusion Model
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From my understanding, the GAN model's training objective is like a minimax game between the generator and discriminator, and the diffusion model's training objective is denoising the data. I see that both models have their respective advantages, so would it be possible to combine the GAN and diffusion model even though they have different architectures and training objectives? If it's possible, how would it work? Would you use one model first and then use the other model to refine the result?
Danny ｜ 2025-08-01 11:41:14💬 Comments section
Hi Danny,
GANs and diffusion models can be combined because they complement each other:
- GANs: Fast generation, sharp images, but can be unstable and prone to mode collapse.
- Diffusion models: Stable and diverse results, but slower and slightly softer images.
Common combination strategies
- Diffusion → GAN refinement: Generate diverse low-res images with diffusion, then upscale and sharpen with a GAN.
- Hybrid training: Add an adversarial loss to the diffusion training to improve detail and realism.
- GAN distillation: Train a GAN to mimic a diffusion model’s output for faster inference.
- GAN as a prior: Use GAN’s latent space as the starting point for diffusion sampling to reduce steps.
Takeaway
- For stability + diversity: start with diffusion.
- For sharpness + speed: refine with a GAN or use a distilled GAN version.
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