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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 15
ACE-Step-v1-chinese-rap-LoRA
ACE-Step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 ↗Ace-Step1.5
ACE-StepProject | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
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查看详情 ↗acestep-5Hz-lm-0.6B
ACE-StepProject | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗acestep-5Hz-lm-4B
ACE-StepProject | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
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查看详情 ↗acestep-captioner
ACE-Step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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗acestep-transcriber
ACE-Step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 ↗acestep-v15-base
ACE-Step🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. 🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️ 🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸
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查看详情 ↗acestep-v15-sft
ACE-Step🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. 🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️ 🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸
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查看详情 ↗acestep-v15-turbo-shift3
ACE-Step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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗acestep-v15-xl-base
ACE-Step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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗acestep-v15-xl-sft
ACE-StepThis is the **XL (4B) SFT** variant of ACE-Step 1.5 — a supervised fine-tuned model with ~4B parameters. SFT provides higher audio quality with CFG (Classifier-Free Guidance) support for fine-grained prompt adherence control.
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查看详情 ↗acestep-v15-xl-sft-diffusers
ACE-Step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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗acestep-v15-xl-turbo
ACE-StepThis is the **XL (4B) Turbo** variant of ACE-Step 1.5 — a distillation-accelerated model that generates high-quality audio in just 8 steps. Combines the speed of turbo with the quality of the 4B architecture.
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查看详情 ↗acestep-v15-xl-turbo-diffusers
ACE-Step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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价格待核验多模态与渠道费用须分别核对
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。