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 the instruct model of Dream 7B, which is an open diffusion large language model with top-tier performance. More details about the model and usage can be found in the blog and github bellow: - **Blog:** https://hkunlp.github.io/blog/2025/dream/ - **Github:** https://github.com/HKUNLP/Dream
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**MinerU-Popo** is a lightweight and universal framework for POst-Processing OCR outputs, bridging the gap between page-level OCR parsing and document-level semantic structure. It construct document tree structure based on with a 4B post-processing model performing four subtasks: table truncation analysis, text truncation analysis, title hierarchy analysis, and image-text association analysis. We handle the challenges of cross-page geometric discontinuity, redundant document parsing and scalability to long documents via: **Basic OCR** | **Before** | **After** :---:|:---:|:---:| MinerU | 53.7 | **90.6** | MonkeyOCR | 48.9 | **87.4** | Dolphin | 60.4 | **83.5** | PaddleOCR | 59.3 | **82.6** | GLM-OCR | 53.5 | **81.8** | **Model** | **TEDS** | **Doc/s** :---:|:---:|:---:| MinerU-Popo | **90.6** | **0.37** | Qwen3-VL-2B | 21.2 | 0.22 | Qwen3-VL-4B | 56.5 | 0.20 | Qwen3-VL-8B | 65.9 | 0.16 | Qwen3-VL-32B | 78.0 | 0.04 |
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An abliterated version of Google's Gemma 3 12B IT created using Heretic. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for video generation models like LTX-2. Check out Gemma 3 12b it Heretic v2 which has vision capabilities, NVFP4 support and decensored with Heretic v1.2.0. It'd be better to use v2. **Note:** GGUF support in ComfyUI for Gemma text encoders is experimental. See PR #402 for status. The GGUFs work with llama.cpp directly.
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An abliterated version of Google's Gemma 3 12B IT created using Heretic v1.3.0. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for video generation models like LTX-2. Available in five quantization formats for ComfyUI - FP8, INT8 (ConvRot row-wise), INT4 (W4A4 ConvRot), NVFP4, and MXFP8 - all produced with SVD-guided learned rounding for maximum fidelity, covering everything from Ada GPUs to Blackwell. You can see the docker, scripts and configurations used to make these files on Heretic Docker Github.
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An abliterated version of Qwen 3 4B created using Heretic v1.2.0. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Z-Image and FLUX.2 Klein 4B. Available in five ComfyUI-native quantized formats (FP8, INT8, INT4, NVFP4, MXFP8), all produced with SVD-guided learned rounding for maximum fidelity. > **Quality:** All quantized variants use SVD-guided learned rounding (AdaRound via convert_to_quant), which optimizes each weight's rounding direction to minimize output reconstruction error — noticeably higher fidelity than naive round-to-nearest quantization. All variants load natively in ComfyUI 0.30.0+ (no plugins) via the `comfy_quant` metadata embedded in each file.
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An abliterated version of Qwen's Qwen3-8B created using Heretic v1.2.0 (git master). This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Klein 9B. Available in five quantization formats for ComfyUI: FP8, INT8 (ConvRot row-wise), INT4 (W4A4 ConvRot), NVFP4, and MXFP8, all produced with SVD-guided learned rounding for maximum fidelity. You can see the docker, scripts and configurations used to make these files on Heretic Docker Github.
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GGUF quantisations of the Heretic abliteration of Qwen3-VL-4B-Instruct. The full forensic report comparing all candidate variants sits in the main repo. Several Heretic trials were run against Qwen3-VL-4B and all of them reach 100% HarmBench ASR, up from 30.8% on the base. This build was picked because, with safety tied, it wins on the tie-breakers: > **Vision note:** these GGUF builds target the text path. For Qwen3-VL vision support, use the bf16 / transformers repo or a ComfyUI checkpoint.
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An abliterated version of Qwen's Qwen3-VL-8B-Instruct created using Heretic v1.3.0. This model has reduced refusals while maintaining model quality. You can see the docker, scripts and configurations used to make these files on Heretic Docker Github. ComfyUI converted text encoders are provided in various formats, along with the original bf16 safetensors. This is a vision-language model (supports both text and image inputs). It does **not** support thinking/reasoning tokens.
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Lucid is a family of AI language models developed and used by DreamGen with a **focus on role-play and story-writing capabilities**. Note that reasoning is mostly intended for assistant tasks (knowledge, math, coding, etc.) and much less for writing. The smaller Lucid models ( `writer character Jon` -> `writer character Amy` -> `user` -> `assistant` -> `writer narration` -> ... - You should use `writer character Jon Doe` when sending a message for a particular character (in this case Jon Doe), **including the user character**. - You should use `user` when providing some instructions (OOC) to the model. The model understands structured scenario information, consisting of plot, style, setting, characters, etc. (this is not an exhaustive list, and all sections are optional).
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Add **anime** to your prompt to make your gens look more anime. Add **photo** to your prompt to make your gens look more photorealistic and have better anatomy. This model was trained on 768x768px images, so use 768x768px, 704x832px, 832x704px, etc. Higher resolution or non-square aspect ratios may produce artifacts. **1girl**, **girl**, etc. give a bit different results, feel free to experiment and see which one you like more! Use this model as well as Dreamlike Diffusion 1.0 and Dreamlike Photoreal 2.0 for free on dreamlike.art!
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You can add **photo** to your prompt to make your gens look more photorealistic. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio. This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px. This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion Pipeline.
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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。
