AuraFlow
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84997
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查看详情 ↗AuraSR-v2
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查看详情 ↗dreamshaper-xl-1-0
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查看详情 ↗**FLUX.2 [dev] Turbo** is a distilled LoRA adapter for [FLUX.2 [dev]](https://huggingface.co/black-forest-labs/FLUX.2-dev) that enables high-quality image generation in just **8 inference steps**. This model inherits the [FLUX [dev] Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/LICENSE.txt) from the base model.
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查看详情 ↗Virtual clothing try-on LoRA for Flux Klein 9b Edit. Given a person image and clothing reference images, generates a realistic result of the person wearing the specified outfit. **Try it now:** - Playground (direct) — upload your images and run the model - Workflow with LLM — auto-generates the prompt for you > Example images shown on this page were generated using Nano Banana Pro and are not part of the training data.
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查看详情 ↗Best paired with the render‑to‑real endpoint's `enable_detail_refine` option (extra 2× spatial‑upscale + detailer pass). File: `3DREAL-strong-v2.safetensors`. Made with Strong v2 — a 1930s chase short where every shot starts as a Blender blockout (action, cameras and cuts owned by the 3D scene): This is an **IC‑LoRA** (in‑context LoRA) for **LTX‑2.3** that turns a rough grey 3D viewport animation (a simple low‑poly blockout) into a fully **photorealistic, film‑quality** video, while keeping the **exact composition, camera move and layout** of the input.
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查看详情 ↗A LoRA adapter for MiniMax H3 specialized in realistic people: faces that hold up in close-up, natural skin texture, believable expressions and gestures, film-style lighting and documentary camera movement. 19 pairs, **same prompt, same seed, adapter on vs off** — the only variable is the LoRA. The trigger word is present on both sides, so it is not doing the work. Each pair plays the base model first, then freezes and dims while the adapted version plays beside it. Close-up talking faces, arguments, several people speaking at once, weathered skin, children, ritual and travel scenes. Nothing cherry-picked from a larger render batch: these are the pairs that were kept, in order. (download the comparison, 197s, 1920x1080)
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查看详情 ↗> **Multi-angle camera control LoRA for Qwen-Image-Edit-2511** > > 96 camera positions • Trained on 3000+ Gaussian Splatting renders • Built with fal.ai **This is the first multi-angle camera control LoRA for Qwen-Image-Edit-2511.** While Qwen-Image-Edit-2511 has built-in viewpoint capabilities, this LoRA provides:
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查看详情 ↗Amateur-Photography
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查看详情 ↗GPasT-small-model
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查看详情 ↗medical_summarization
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查看详情 ↗nsfw_image_detection
FalconsaiThe **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks. During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. The model was fine-tuned with a judiciously chosen batch size of 16. This choice not only balanced computational efficiency but also allowed for the model to effectively process and learn from a diverse array of images. To facilitate this fine-tuning process, a learning rate of 5e-5 was employed. The learning rate serves as a critical tuning parameter that dictates the magnitude of adjustments made to the model's parameters during training. In this case, a learning rate of 5e-5 was selected to strike a harmonious balance between rapid convergence and steady optimization, resulting in a model that not only learns swiftly but also steadily refines its capabilities throughout the training process.
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查看详情 ↗nsfw_image_detection_26
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查看详情 ↗text_summarization
FalconsaiThe **Fine-Tuned T5 Small** is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text. The model, named "t5-small," is pre-trained on a diverse corpus of text data, enabling it to capture essential information and generate meaningful summaries. Fine-tuning is conducted with careful attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance for text summarization. During the fine-tuning process, a batch size of 8 is chosen for efficient computation and learning. Additionally, a learning rate of 2e-5 is selected to balance convergence speed and model optimization. This approach guarantees not only rapid learning but also continuous refinement during training.
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查看详情 ↗MN-Violet-Lotus-12B
FallenMerick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 ↗Qwen3.8-27B-Uncensored-GP100-GGUF
fallentree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 ↗R1-Onevision-7B
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查看详情 ↗big-asp-v2
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查看详情 ↗bigasp-v1
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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。