Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still significant potential for improvement in current text-to-image inpainting models, particularly in better aligning the inpainted area with user prompts. Therefore, we introduce $\textit{HD-Painter}$, a $\textbf{training-free}$ approach that $\textbf{accurately follows prompts}$. To this end, we design the $\textit{Prompt-Aware Introverted Attention (PAIntA)}$ layer enhancing self-attention scores by prompt information resulting in better text aligned generations. To further improve the prompt coherence we introduce the $\textit{Reweighting Attention Score Guidance (RASG)}$ mechanism seamlessly integrating a post-hoc sampling strategy into the general form of DDIM to prevent out-of-distribution latent shifts. Our experiments demonstrate that HD-Painter surpasses existing state-of-the-art approaches quantitatively and qualitatively across multiple metrics and a user study. Code is publicly available at: [https://github.com/Picsart-AI-Research/HD-Painter](https://github.com/Picsart-AI-Research/HD-Painter)

We present Zero-Painter, a novel training-free framework for layout-conditional text-to-image synthesis that facilitates the creation of detailed and controlled imagery from textual prompts. Our method utilizes object masks and individual descriptions, coupled with a global text prompt, to generate images with high fidelity. Zero-Painter employs a two-stage process involving our novel Prompt-Adjusted Cross-Attention (PACA) and Region-Grouped Cross-Attention (ReGCA) blocks, ensuring precise alignment of generated objects with textual prompts and mask shapes. Our extensive experiments demonstrate that Zero-Painter surpasses current state-of-the-art methods in preserving textual details and adhering to mask shapes.

The existing image-to-video translation methods generally follow a frame-by-frame generative paradigm, while extracting the temporal information from a reference video or an audio stream. Inspired by the recent success in text-guided image generation, we explore a more challenging but promising task, Text-guided Image-to-Video (TI2V) translation. Given an image and a brief text description as input, TI2V aims to generate a facial expression video following the image and text. To this end, we first propose an automatic video captioning pipeline to generate dense textual descriptions for facial video datasets, using both expression labels and action units. These dense textual descriptions provide precise semantic guidance for TI2V learning. Then we design and train an efficient framework, FaceCLIP, on these datasets to deal with the TI2V translation task. FaceCLIP adopts a video autoencoder to model the temporal information of training videos, and a pretrained CLIP model to embed the video frames and the text description. We design a reconstruction loss and an embedding alignment loss to train the autoencoder to obtain the text-guided video generative ability. Recognizing that expressions are closely tied to facial landmark motions, the reconstruction loss is applied to facial landmarks rather than each video frame, significantly enhancing training efficiency. We compare FaceCLIP with several potential baseline methods, and extensively evaluate the performance using multiple metrics. Both qualitative and quantitative results validate the superiority of FaceCLIP in terms of both visual quality and expression-text consistency. Moreover, the unique ability of FaceCLIP to generate videos based on abstract texts demonstrates its stronger generalization capability.