RITA_s
lightonai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。
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探索 AI 模型 85001
SlideOn
lightonai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。
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LightOriginsHQ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。
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gemma-3-12b-it-qat-q4_0-unquantized
Lightricks> [!Note] > This repository corresponds to the 12B **instruction-tuned** version of the Gemma 3 model using Quantization Aware Training (QAT). > > **The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool** > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
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查看详情 ↗LTX-2
LightricksThis model card focuses on the LTX-2 model, as presented in the paper LTX-2: Efficient Joint Audio-Visual Foundation Model. The codebase is available here. 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. - **Developed by:** Lightricks - **Model type:** Diffusion-based audio-video foundation model - **Language(s):** English
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查看详情 ↗LTX-2-19b-LoRA-Camera-Control-Dolly-In
LightricksThis is a **camera-motion control LoRA** trained to bias generations toward a **dolly-in camera move** (camera pushes forward toward the subject, often increasing subject scale and reducing background prominence) on top of an **LTX-2 19B** base video model. See the base model license: **LTX-2 Community License** (`https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2`).
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查看详情 ↗LTX-2-19b-LoRA-Camera-Control-Dolly-Left
LightricksThis is a camera-motion control **LoRA** trained to bias generations toward a **dolly-left** camera move (camera translates laterally to the left, revealing content on the left side of the scene and changing parallax) on top of an **LTX-2 19B** base video model. To get the best results with Motion Camera LoRAs, describe the **destination of the movement** and the **hidden parts of the scene** so the model knows what to reveal as the frame shifts. For a dolly-left shot, explicitly mention what should come into view on the **left side** during the move (e.g., the far-left side of a room, objects/people positioned off-frame to the left, background structures that become visible due to parallax). This gives the model a visual “map” of what exists as the camera slides left.
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查看详情 ↗LTX-2-19b-LoRA-Camera-Control-Jib-Down
LightricksThis is a camera-motion control LoRA trained to bias generations toward a **jib-down** camera move on top of an **LTX-2 19B** base video model. To get the best results with Motion Camera LoRAs, describe the **destination of the movement** and the **hidden parts of the scene** so the model knows what to reveal as the frame shifts. For a **jib-down** shot, explicitly mention what should come into view **near the bottom of the frame** as the camera descends (e.g., floor details, objects placed low, signage near ground level, lower levels of architecture). This gives the model a visual “map” of what exists below the initial framing.
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查看详情 ↗LTX-2-19b-LoRA-Camera-Control-Jib-Up
LightricksThis is a **camera-motion control LoRA** trained to bias generations toward a **jib-up camera move** (camera rises vertically) on top of an **LTX-2 19B** base video model. See the base model license: **LTX-2 Community License** (`https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2`).
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查看详情 ↗LTX-2-19b-LoRA-Camera-Control-Static
LightricksThis is a camera-motion control **LoRA** trained to bias generations toward a **static camera** (no intentional camera translation, pan, tilt, roll, or zoom) on top of an **LTX-2 19B** base video model.
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查看详情 ↗LTX-2.3
LightricksThis 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. 💻💻 **If you want to dive in right to the code - it is available here.** 💾💾 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.
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Clean-Plate
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Cross-Eyed
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Deblur
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-DubIt
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-HDR
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-In-Outpainting
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Ingredients
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Instant-Shave
Lightricks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。
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查看详情 ↗LTX-2.3-22b-IC-LoRA-Relight
Lightricks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。
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查看详情 ↗LTX-2.3-22b-LoRA-Foley-V2A
Lightricks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。
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查看详情 ↗LTX-2.3-fp8
Lightricks**This is the FP8 versions of the LTX-2.3 model. All information below is derived from the base model.** 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. 💻💻 **If you want to dive in right to the code - it is available here.** 💾💾
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。