End To End Diffusion Latent Optimization Improves Classifier Guidance

End To End Diffusion Latent Optimization Improves Classifier Guidance Classifier guidance using the gradients of an image classifier to steer the generations of a diffusion model has the potential to dramatically expand the creative control over image generation and editing. What is doodl? doodl (direct optimization of diffusion latents) is a variant of classifier guidance that directly optimizes diffusion latents x t instead of using model based gradients to guide denoising.

Mid U Guidance Fast Classifier Guidance For Latent Diffusion Models 基于这种近似的缺点，本文提出了一个新颖的引导方法：direct optimization of diffusion latents (doodl)。 该方法通过优化扩散潜变量相对于预训练分类器在真实生成的像素上的梯度，利用可逆扩散过程实现内存高效的反向传播，实现即插即用的引导。. End to end diffusion latent optimization improves classifier guidance abstract: classifier guidance—using the gradients of an image classifier to steer the generations of a diffusion model—has the potential to dramatically expand the creative control over image generation and editing. Classifier guidance—using the gradients of an image classifier to steer the generations of a diffusion model—has the potential to dramatically expand the creative control over image generation and editing. Bibliographic details on end to end diffusion latent optimization improves classifier guidance.

Mid U Guidance Fast Classifier Guidance For Latent Diffusion Models Classifier guidance—using the gradients of an image classifier to steer the generations of a diffusion model—has the potential to dramatically expand the creative control over image generation and editing. Bibliographic details on end to end diffusion latent optimization improves classifier guidance. While training both components jointly with standard diffusion loss is observed to be ineffective — often degrading final performance — we show that this limitation can be overcome using a simple representation alignment (repa) loss. Eading to sub optimal guidance signal. to enable flexible and exact model guidance, without noise aware classifiers or approximations, we propose di rect opt. mization of diffusion latents (doodl). doodl optimizes the initial diffusion noise vectors w.r.t. a model based loss on images generated. To avoid retraining an additional model, our method directly optimizes the diffusion latents. besides, sgool utilizes an invertible diffusion process and endows it with the merits of constant memory implementation. hence, our method becomes a parameter efficient and plug and play fine tuning method. Classifier guidance – using the gradients of an image classifier to steer the generations of a diffusion model – has the potential to dramatically expand the creative control over image generation and editing.

Mid U Guidance Fast Classifier Guidance For Latent Diffusion Models While training both components jointly with standard diffusion loss is observed to be ineffective — often degrading final performance — we show that this limitation can be overcome using a simple representation alignment (repa) loss. Eading to sub optimal guidance signal. to enable flexible and exact model guidance, without noise aware classifiers or approximations, we propose di rect opt. mization of diffusion latents (doodl). doodl optimizes the initial diffusion noise vectors w.r.t. a model based loss on images generated. To avoid retraining an additional model, our method directly optimizes the diffusion latents. besides, sgool utilizes an invertible diffusion process and endows it with the merits of constant memory implementation. hence, our method becomes a parameter efficient and plug and play fine tuning method. Classifier guidance – using the gradients of an image classifier to steer the generations of a diffusion model – has the potential to dramatically expand the creative control over image generation and editing.

Mid U Guidance Fast Classifier Guidance For Latent Diffusion Models To avoid retraining an additional model, our method directly optimizes the diffusion latents. besides, sgool utilizes an invertible diffusion process and endows it with the merits of constant memory implementation. hence, our method becomes a parameter efficient and plug and play fine tuning method. Classifier guidance – using the gradients of an image classifier to steer the generations of a diffusion model – has the potential to dramatically expand the creative control over image generation and editing.

Diffusion Classifier