Search Results for "background"

Step1X-Edit is a state-of-the-art open-source image editing model/framework that uses a multimodal large language model (LLM) together with a diffusion-based image decoder to let users edit images simply via natural-language instructions plus a reference image. You supply an existing image and a textual command — e.g. “add a ruby pendant on the girl’s neck” or “make the background a sunset over mountains” — and the model interprets the instruction, computes a latent embedding combining the image content and user intent, then decodes a new image implementing the edit. The model targets general-purpose editing: from object addition/removal, style changes, recoloring, retouching, background replacement, to complex transformations like changing lighting, mood, or art style. ...

...Beyond straightforward text-to-music synthesis, ACE-Step 1.5 enables flexible creative workflows, including tasks like cover generation, editing existing tracks, transforming vocals to background accompaniment, and stylistic personalization using low-rank adaptation from just a few example songs.

Gorden Super PPT Skills is an AI presentation skill package for generating high-density visual presentations and converting them into editable PowerPoint files. The workflow is split into three skills that can be used separately or together. One skill generates image-based presentation pages from a topic or content brief. Another skill reconstructs image slides into editable PPTX files by separating backgrounds, layout structures, icons, decorations, and text. The orchestration skill chains...

Qwen-Image-Layered is an extension of the Qwen series of multimodal models that introduces layered image understanding, enabling the model to reason about hierarchical visual structures — such as separating foreground, background, objects, and contextual layers within an image. This architecture allows richer semantic interpretation, enabling use cases such as scene decomposition, object-level editing, layered captioning, and more fine-grained multimodal reasoning than with flat image encodings alone. By combining text and structured image representations, it aims to facilitate tasks where both descriptive and structural understanding are important, such as detailed image QA, interactive image editing via prompt layers, and image-conditioned generation with structural control. ...

...The models support translation across 33 languages and are designed to follow detailed translation instructions in multiple languages. They can handle tasks involving terminology, style, personalization, delimiters, structured data, and background-aware translation. The repository also provides model links, inference guidance, deployment examples, training documentation, and quantization resources. Overall, Hy-MT2 is useful for developers and researchers who need flexible, instruction-following machine translation models for production, evaluation, or experimentation.

...The repository releases the model, training recipe, time-aligned interaction data, and deployable system together. Its system includes inference, WebUI, ASR, TTS, and background-agent services running on vLLM-based infrastructure. It is useful for real-time monitoring, live commentary, cooking guidance, game calling, visual alerts, and other scenarios where an AI should respond at the right moment.

...Developed by researchers at Facebook (Meta AI), ToMe introduces an efficient technique that merges similar tokens within transformer layers, reducing redundant computation while preserving model accuracy. This approach differs from token pruning, which removes background tokens entirely; instead, ToMe merges tokens based on feature similarity, allowing it to compress both foreground and background information efficiently. ToMe integrates seamlessly into existing transformer models such as DeiT, MAE, SWAG, and timm ViTs, offering 2–3x speedups during inference and substantial efficiency gains during training. ...