Bernini-R-GGUF-ComfyUI
vantagewithai基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
The FastH3 8-Step V2 checkpoint from FastVideo. It generates synchronized video and audio from text with eight transformer forwards. This step-1300 model was trained with data-free DMD2 and VSA-H3 at 80% sparsity. > This checkpoint requires FastVideo's VSA-H3 attention backend. Its video > scheduler shift is 10, not the base model's 12. > This checkpoint supports text-to-audio-video generation. FL2VA and Ref2VA were not distilled. Difficult motion, fine detail, and some audio may remain below the base MiniMax H3 model. This checkpoint inherits the MiniMax H3 Community License.
**This is the final BF16 release checkpoint of FastH3 Omni.** It generates video with synchronized audio from a text prompt and ordered image, video and audio references, using eight transformer evaluations. For server deployment, use this BF16 checkpoint with UniServe's reference-conditioned (`ref2va`) configurations. The guide covers installation, model downloads, GPU placement, request capacities and the video/audio API. For the BF16 deployment, choose UniServe's `quality` precision preset. NVFP4 presets require Blackwell hardware. Use **FastVideo** with the NVFP4 QAD variant for local inference. QAD means quantization-aware distillation; these weights are trained for NVFP4 execution on Blackwell. Start with the FastVideo cookbook and FastH3 Omni recipe builder.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
We introduce FireRed-Image-Edit-1.1, an upgrade to our general-purpose image editing foundation model. Building upon the capabilities presented in the FireRed-Image-Edit-1.0 Technical Report, version 1.1 significantly enhances identity consistency, multi-image conditioning, and domain-specialized editing performance, bringing the model closer to real-world creative production needs.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
See our collection of in-house trained LoRAs trained on Raw and meant to be used with Turbo: Krea-2 LoRA Collection Krea 2 is a text-to-image diffusion model that generates images from natural-language text descriptions. The model is designed to support creative, commercial, developer, and research use cases, including image generation, concepting, design exploration, visual production workflows, and integration into applications and creative tools. This model was developed using a combination of publicly available data, data licensed from third-party providers, and synthetic data generated through proprietary methods. The training data includes images and their associated captions or text descriptions.
See our collection of in-house trained LoRAs trained on Raw and meant to be used with Turbo: Krea-2 LoRA Collection Krea 2 is a text-to-image diffusion model that generates images from natural-language text descriptions. The model is designed to support creative, commercial, developer, and research use cases, including image generation, concepting, design exploration, visual production workflows, and integration into applications and creative tools. This model was developed using a combination of publicly available data, data licensed from third-party providers, and synthetic data generated through proprietary methods. The training data includes images and their associated captions or text descriptions.
**Watch us on Youtube:** @VantageWithAI See our collection of in-house trained LoRAs trained on Raw and meant to be used with Turbo: Krea-2 LoRA Collection - **Model Name:** Krea 2 - **Version:** v1.0 - **Release Date:** June 22, 2026 - **Model Type:** Text-to-image diffusion model - **Architecture:** Diffusion Transformer with 12 billion parameters - **License:** Krea 2 Community License - **Release Format:** Open-weight release and Krea-hosted product integrations - **Model Developer:** Krea.ai, Inc.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
We introduce **LongCat-Image-Edit**, the image editing version of Longcat-Image. LongCat-Image-Edit supports bilingual (Chinese-English) editing, achieves state-of-the-art performance among open-source image editing models, delivering leading instruction-following and image quality with superior visual consistency.
We introduce **LongCat-Image-Edit-Turbo**, the distilled version of LongCat-Image-Edit. It achieves high-quality image editing with only 8 NFEs (Number of Function Evaluations) , offering extremely low inference latency.
We introduce **LongCat-Image**, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models.
**Original model Link:** https://huggingface.co/meituan-longcat/LongCat-Video-Avatar-1.5 **Watch us at Youtube:** @VantageWithAI We are excited to announce the release of LongCat-Video-Avatar 1.5, an upgraded open-source framework that prioritizes extreme empirical optimization and production-readiness for audio-driven human video generation. Built upon the LongCat-Video foundation model, v1.5 delivers highly stable, commercial-grade avatar video synthesis supporting native tasks including Audio-Text-to-Video (AT2V), Audio-Text-Image-to-Video (ATI2V), and Video Continuation, with seamless compatibility for both single-stream and multi-stream audio inputs.
We are excited to announce the release of LongCat-Video-Avatar, a unified model that delivers expressive and highly dynamic audio-driven character animation, supporting native tasks including Audio-Text-to-Video, Audio-Text-Image-to-Video, and Video Continuation with seamless compatibility for both single-stream and multi-stream audio inputs. --> The following videos showcase example generations from our model and have been compressed for easier viewing. Human evaluation on naturalness and realism of the synthesized videos. The benchmark EvalTalker [1] contains more than 400 testing samples with different difficulty levels for evaluating the single and multiple human video generation.
This model card focuses on the LTX-2.3 model, which is a significant update to the LTX-2 model with improved audio and visual quality as well as enhanced prompt adherence. LTX-2 was presented in the paper LTX-2: Efficient Joint Audio-Visual Foundation Model. LTX-2.3 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution. It's extremely easy to reproduce the LoRAs and IC-LoRAs we publish with the model by following the instructions on the LTX-2 Trainer Readme.
**Original model Link:** https://huggingface.co/Lightricks/LTX-2.5 **Watch us at Youtube:** @VantageWithAI All the files have been quantized using **mixed-precision levels** to maintain the highest possible quality, at the expense of a slight increase in file size.
LTX-2 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution. LTX-2 is accessible right away via the following links: - LTX-Studio text-to-video - LTX-Studio image-to-video You can use the models - full, distilled, upscalers and any derivatives of the models - for purposes under the license.
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.