Search Results for "visual-" - Page 17

...Built on a 12-billion-parameter Diffusion Transformer (DiT), it is a distilled and post-trained version of the Krea 2 Raw checkpoint, enabling photorealistic and artistic image synthesis in as few as eight inference steps. Designed for creative professionals, developers, and researchers, it supports concept art, design exploration, marketing assets, illustrations, and commercial visual production. Unlike the Raw checkpoint, which is intended for fine-tuning and LoRA training, Turbo is optimized for direct inference, delivering native resolutions up to 2K with excellent prompt adherence and broad aesthetic diversity. The model integrates with Diffusers, SGLang, ComfyUI, and other modern inference frameworks, and supports LoRAs trained on the Raw model.

FLUX.1-Krea-dev is a 12 billion parameter rectified flow transformer for text-to-image generation, developed by Black Forest Labs in collaboration with Krea. It delivers aesthetic, high-quality outputs focused on photography and visual coherence, making it a strong competitor to closed-source models. Trained using guidance distillation, it offers efficient inference while preserving creative fidelity. The model is distributed under a non-commercial license, with conditions to prevent misuse and support ethical AI development. FLUX.1-Krea-dev is available via Diffusers and ComfyUI, and integrates with the FluxPipeline for streamlined usage. ...

Qwen-Image-Edit is the image editing extension of Qwen-Image, a 20B parameter model that combines advanced visual and text-rendering capabilities for creative and precise editing. It leverages both Qwen2.5-VL for semantic control and a VAE Encoder for appearance control, enabling users to edit at both the content and detail level. The model excels at semantic edits like style transfer, object rotation, and novel view synthesis, while also handling precise appearance edits such as adding or removing elements without altering surrounding regions. ...

...It takes camera images and natural language instructions as input and outputs normalized 7-DoF robot actions, enabling control of multiple robot types across various domains. Built on top of LLaMA-2 and DINOv2/SigLIP visual backbones, it allows both zero-shot inference for known robot setups and parameter-efficient fine-tuning for new domains. The model supports real-world robotics tasks, with robust generalization to environments seen in pretraining. Its actions include delta values for position, orientation, and gripper status, and can be un-normalized based on robot-specific statistics. ...

...It supports multilingual fashion queries and works best with clean, product-style images against white backgrounds. The model can be used for product search, recommendation systems, or visual tagging in e-commerce platforms.

Ministral 3 3B Base 2512 is the smallest model in the Ministral 3 family, offering a compact yet capable multimodal architecture suited for lightweight AI applications. It combines a 3.4B-parameter language model with a 0.4B vision encoder, enabling both text and image understanding in a tiny footprint. As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks....

Ministral 3 3B Instruct 2512 is the smallest model in the Ministral 3 family, offering a lightweight yet capable multimodal architecture designed for edge and low-resource deployments. It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its small size, it delivers efficient real-time performance and can run locally on a single 8GB GPU, with further memory reductions through quantization. ...