言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
This is a 9B model whose architecture is deepseek-v3, trained from scratch using 350B+ tokens from fully open-source, English-only datasets. It is designed for development and debugging purposes within the open-source community.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
Occamy-1.0 · APEX GGUF Three plain profiles · four calibrated profiles, including Mini Collection · Checkpoint explorer · Project · Paper Occamy-1.0 quantized with the LocalAI team's APEX profiles. APEX allocates GGUF precision by tensor role and layer position: routed experts, shared experts and attention receive different allocations. The three plain profiles and four calibrated `I-` profiles use the pinned upstream 40-layer configurations. Every file is generated directly from the same BF16 source. The calibrated group adds I-Compact, I-Quality, I-Balanced and I-Mini. Profile names describe allocation strategies; file size does not follow the name order. These are weight-file sizes, not peak memory requirements. Allow additional memory for the runtime, context and image input.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
Occamy-1.0 · GGUF 17 quantizations · llama.cpp · optional vision projector Model collection · Checkpoint explorer · Project · Paper GGUF quantizations of Accio-Lab/occamy-1.0. **Q3_K_S, Q5_K_S, Q3_K_L, Q4_0 and Q5_0** are direct BF16 conversions with standard recipes and no importance matrix. The earlier ten additions used calibration importance matrices; Q4_K_M and Q8_0 retain their historical recipes. Labels describe mixed precision rather than uniform bits for every tensor. Previously published weights and evidence are preserved. Separate APEX GGUF profiles provide seven additional MoE mixed-precision allocations: three plain profiles and four calibrated I profiles, including Mini. Compare all 24 GGUF options in the checkpoint explorer.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
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。
基本情報を収録・詳細は順次追加
Project | 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.
翻訳待ち・原文を表示
Project | 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.
翻訳待ち・原文を表示
Project | 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.
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
🚀 **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. 🎸
翻訳待ち・原文を表示
🚀 **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. 🎸
翻訳待ち・原文を表示
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。
基本情報を収録・詳細は順次追加
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。
This 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.
翻訳待ち・原文を表示
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
