In detail, we first train an image projection module to connect a vision encoder with LLM.
Furthermore, we significantly improve the naturalness and speaker similarity of synthetic speech even in zero-shot speech synthesis scenarios.
Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents.
However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality interaction, which is not effective in prompting object detection and visual grounding.
Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step.
For appearance control, we borrow intermediate latents and their features from the text-to-image (T2I) generation for ensuring the generated first frame is equal to the given generated image.
Single image depth estimation is a foundational task in computer vision and generative modeling.
Agent-based modeling has been around for decades, and applied widely across the social and natural sciences.
We also introduce latent DiffiT which consists of transformer model with the proposed self-attention layers, for high-resolution image generation.
Ranked #2 on Image Generation on ImageNet 256x256