Stable Diffusion show that our method outperforms manual prompt engineering in terms of both automatic metrics and human preference ratings. Moreover, reinforcement learning further boosts performance, especially on out-of-domain prompts. The pretrained checkpoints are available atthis https URL...
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets Team: Stability AI. Andreas Blattmann, Tim Dockhorn, Sumith Kulal, et al., Robin Rombach arXiv, 2023.11 [Paper], [PDF], [Code] FusionFrames: Efficient Architectural Aspects for Text-to-Video Generation Pipel...
Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new
二、关键词 Text to Image, Generative Adversarial Network, Image Synthesis, Computer Vision 三、为什么要提出StackGAN-v2? 通过在多个尺度上建模数据分布,如果这些模型分布中的任何一个与该尺度上的真实数据分布共享支持,则堆叠结构可以提供良好的梯度信号,以加速或稳定整个网络在多个尺度上的训练。例如,在第一层近...
In this paper, we present Modality Adaptation with text-to-image Diffusion Models (MADM). With the powerful generalization of Text-to-Image Diffusion Models (TIDMs), we extend domain adaptation to modality adaptation, aiming to segment other unexplored visual modalities in the real-world....
This can be challenging as the updated text-to-image mapping might easily overfit the few available images. In our experiments, we use Stable Diffusion [1] as our backbone model, which is built on the Latent Diffusion Model (LDM) [57]. LDM first encodes images into a late...
Recent large text-to-image models such as Imagen [58], DALL-E2 [51], Parti [69], CogView2 [17] and Stable Diffusion [55] demon- strated unprecedented semantic generation. These models do not provide fine-grained control over a generated image ...
(e.g., stable diffusion) and corresponding personalization techniques such as dreambooth and lora, everyone can manifest their imagination into high-quality images at an affordable cost. however, adding motion dynamics to existing high-quality personalized t2is and enabling them to generate animations...
论文使用四个Nvidia RTX 3090 GPU和PyTorch库进行实验。为了计算SDS损失,利用了通过Hugging Face Diffusers实现的Stable Diffusion模型。对于DMTET和material编码器,将它们分别实现为两层MLP和单层MLP,隐藏层维度为32。 从椭球体开始进行t...
Stable Documentation of the stable (i.e. most recent release) Install NeMo Framework The NeMo Framework can be installed in a variety of ways, depending on your needs. Depending on the domain, you may find one of the following installation methods more suitable. ...