Diffusion models excel at generating high-quality images and are easy to
extend, making them extremely popular among active users who have created an
extensive collection of diffusion models with various styles by fine-tuning
base models such as Stable Diffusion. Recent work has focused on uncovering
semantic and visual information encoded in various components of a diffusion
model, enabling better generation quality and more fine-grained control.
However, those methods target improving a single model and overlook the vastly
available collection of fine-tuned diffusion models. In this work, we study the
combinations of diffusion models. We propose Diffusion Cocktail (Ditail), a
training-free method that can accurately transfer content information between
two diffusion models. This allows us to perform diverse generations using a set
of diffusion models, resulting in novel images that are unlikely to be obtained
by a single model alone. We also explore utilizing Ditail for style transfer,
with the target style set by a diffusion model instead of an image. Ditail
offers a more detailed manipulation of the diffusion generation, thereby
enabling the vast community to integrate various styles and contents seamlessly
and generate any content of any style.

通过使用不同的扩增模型，本论文提出了一种名为 Diffusion Cocktail (Ditail) 的训练 - free 方法，可以准确地在两个扩增模型之间传递内容信息，实现多样化的图像生成和风格转移。

扩散鸡尾酒：扩散模型的融合生成

Diffusion Cocktail: Fused Generation from Diffusion Models

We propose a simple yet highly effective method that addresses the
mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).
Although conditional distributions are multi-modal (i.e., having many modes) in
practice, most cGAN approaches tend to learn an overly simplified distribution
where an input is always mapped to a single output regardless of variations in
latent code. To address such issue, we propose to explicitly regularize the
generator to produce diverse outputs depending on latent codes. The proposed
regularization is simple, general, and can be easily integrated into most
conditional GAN objectives. Additionally, explicit regularization on generator
allows our method to control a balance between visual quality and diversity. We
demonstrate the effectiveness of our method on three conditional generation
tasks: image-to-image translation, image inpainting, and future video
prediction. We show that simple addition of our regularization to existing
models leads to surprisingly diverse generations, substantially outperforming
the previous approaches for multi-modal conditional generation specifically
designed in each individual task.

这篇论文提出了一种简单而高效的方法来解决条件生成式对抗网络（cGAN）中的模式崩塌问题，它通过显式地规范化生成器以产生不同的输出来控制可变因素，从而在视觉质量和多样性之间实现平衡，这种方法在图像翻译、图像修补、未来预测等多个条件生成任务中取得了出色的效果。