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Loading...Introduction to Image Synthesis
Last quarter, our team discovered that generating high-quality images using deep learning models was more challenging than expected. We tried several approaches, including Generative Adversarial Networks (GANs) and Diffusion Models. Here's what I learned when comparing these two techniques for image synthesis using PyTorch 2.0 and Hugging Face Transformers 5.10.
Background on GANs and Diffusion Models
Most docs skip the hard part of explaining how GANs and Diffusion Models actually work under the hood. I realized that understanding the architecture and training process of these models is crucial for achieving good results. GANs consist of a generator and a discriminator, which are trained simultaneously to produce realistic images. Diffusion Models, on the other hand, use a process called denoising to generate images.
Implementing GANs with PyTorch 2.0
When I first tried implementing GANs with PyTorch 2.
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- Complete step-by-step implementation guide
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- Real-world examples and metrics
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