We present a novel, training-free approach for textual editing of real images
using diffusion models. Unlike prior methods that rely on computationally
expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA)
to efficiently preserve image details. We demonstrate how the diffusion process
is amenable to spatial guidance using a reference image, leading to
semantically coherent edits. This eliminates the need for complex optimization
and costly model finetuning, resulting in significantly faster editing compared
to previous methods. Additionally, our method avoids the storage requirements
associated with large finetuned models. These advantages make our approach
particularly well-suited for editing on mobile devices and applications
demanding rapid response times. While simple and fast, our method achieves
62-71\% preference in a user-study and significantly better model-based editing
strength and image preservation scores.

我们提出了一种新颖的，无需训练的方法，用于实现基于扩散模型的真实图像的文本编辑。我们的方法利用潜在空间对齐（LASPA）有效地保留图像细节，通过扩散过程与参考图像进行空间引导，从而实现语义上连贯的编辑。该方法无需复杂的优化和昂贵的模型微调，与之前的方法相比，编辑速度显著提高。此外，我们的方法避免了大型微调模型的存储要求。这些优势使得我们的方法特别适用于移动设备和需要快速响应时间的应用。尽管简单快速，我们的方法在用户研究中获得了 62-71％的偏好，并且在模型编辑强度和图像保护评分方面表现显著优越。

LASPA：用于快速训练无需标记的单图像编辑的潜在空间对齐

LASPA: Latent Spatial Alignment for Fast Training-free Single Image  Editing

Image-to-video (I2V) generation tasks always suffer from keeping high
fidelity in the open domains. Traditional image animation techniques primarily
focus on specific domains such as faces or human poses, making them difficult
to generalize to open domains. Several recent I2V frameworks based on diffusion
models can generate dynamic content for open domain images but fail to maintain
fidelity. We found that two main factors of low fidelity are the loss of image
details and the noise prediction biases during the denoising process. To this
end, we propose an effective method that can be applied to mainstream video
diffusion models. This method achieves high fidelity based on supplementing
more precise image information and noise rectification. Specifically, given a
specified image, our method first adds noise to the input image latent to keep
more details, then denoises the noisy latent with proper rectification to
alleviate the noise prediction biases. Our method is tuning-free and
plug-and-play. The experimental results demonstrate the effectiveness of our
approach in improving the fidelity of generated videos. For more image-to-video
generated results, please refer to the project website:
this https URL

提出了一种用于图像到视频生成的方法，通过添加更多精确的图像信息和噪声修正，提高生成视频的保真度。

高保真图像到视频生成的无需调参噪声矫正

Tuning-Free Noise Rectification for High Fidelity Image-to-Video  Generation

We introduce "ImageDream," an innovative image-prompt, multi-view diffusion
model for 3D object generation. ImageDream stands out for its ability to
produce 3D models of higher quality compared to existing state-of-the-art,
image-conditioned methods. Our approach utilizes a canonical camera
coordination for the objects in images, improving visual geometry accuracy. The
model is designed with various levels of control at each block inside the
diffusion model based on the input image, where global control shapes the
overall object layout and local control fine-tunes the image details. The
effectiveness of ImageDream is demonstrated through extensive evaluations using
a standard prompt list. For more information, visit our project page at
this https URL

使用 ImageDream 创新的图像提示多视角扩散模型进行 3D 物体生成，通过提高视觉几何准确性来产生比现有最先进的图像条件方法更高质量的 3D 模型。

ImageDream: 图像启发式多视角扩散用于 3D 生成

ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation

High-quality dehazing performance is highly dependent upon the accurate
estimation of transmission map. In this work, the coarse estimation version is
first obtained by weightedly fusing two different transmission maps, which are
generated from foreground and sky regions, respectively. A hybrid variational
model with promoted regularization terms is then proposed to assisting in
refining transmission map. The resulting complicated optimization problem is
effectively solved via an alternating direction algorithm. The final haze-free
image can be effectively obtained according to the refined transmission map and
atmospheric scattering model. Our dehazing framework has the capacity of
preserving important image details while suppressing undesirable artifacts,
even for hazy images with large sky regions. Experiments on both synthetic and
realistic images have illustrated that the proposed method is competitive with
or even outperforms the state-of-the-art dehazing techniques under different
imaging conditions.

本文提出了一种利用两个不同的透射率图加权融合的粗略估计版本以及增强正则化项的混合变分模型进行精细调节透射率图的去雾算法，并成功地应用于保留重要图像细节和抑制不良伪影的图像去雾成像中，具有很高的竞争力。