

Diffusionrl
Overview :
Text-to-image diffusion models are a class of deep generative models that have demonstrated impressive image generation capabilities. However, these models are susceptible to the implicit biases present in the webpage-scale text-image training pairs, which may not accurately model the aspects of images that we care about. This can lead to suboptimal samples, model biases, and images that are incongruent with human ethics and preferences.
This work presents an effective and scalable algorithm that leverages reinforcement learning (RL) to improve diffusion models, encompassing a diverse range of reward functions such as human preference, coherence, and fairness, covering millions of images. We demonstrate how our method significantly outperforms existing approaches, aligning diffusion models with human preferences. We further illustrate how it substantially improves the pretrained Stable Diffusion (SD) model, resulting in samples preferred by humans by 80.3% while also enhancing the compositional and diversity of generated samples.
Target Users :
Improves the generation quality of text-to-image diffusion models, enhancing the human preference, coherence, and diversity of the generated images.
Use Cases
DiffusionRL improves the quality of text-to-image diffusion models.
DiffusionRL applied to Stable Diffusion model, making the generated samples more aligned with human preferences.
Utilizing the reinforcement learning algorithm of DiffusionRL, the generation quality of diffusion models is improved, enhancing the diversity of the images.
Features
Improves diffusion models
Uses reinforcement learning to improve diffusion models
Encompasses a diverse range of reward functions
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