crayon_style_lora_sdxl
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 22
face-helper-sdxl-lora
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查看详情 ↗Flex.1-alpha
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查看详情 ↗Flex.2-preview
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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 ↗flux2_berthe_morisot
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查看详情 ↗This LoRA will turn Ideogram 4 into a few step model. It is capable of generating good quality images in as little as 2 steps with no CFG and no unconditional model. It was created with a continuious turbo training method I am working on and still perfecting. I will release more information on that once it is fully polished. This will likely recieve updated versions as the method improves.
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查看详情 ↗This LoRA will allow you to inference on the Krea2 Turbo model in the style of a reference image. It was trained with AI Toolkit using the experimental Krea2 reference image trainer with the turbo training adapter. It was trained on thousands of hand curated style pairs I have been building over the years. It was trained to handle 1-2 reference images. For inference in Comfy UI you will need this custom node. ComfyUI-Krea2-Ostris-Edit Use the template in that repo with this lora for the effect. No trigger word is needed just image and prompt. For diffusers, there is a communiuty pipeline here ostris/Krea2OstrisEdit. You can run it like below:
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查看详情 ↗This is a training adapter designed to be used for fine-tuning krea/Krea-2-Turbo. It was made for use with AI Toolkit but could potentially be used in other trainers as well. If you are implementing it into training code and have questions. I am always happy to help. Just reach out. When you train directly on a step distilled model, the distillation breaks down very quickly. This results in losing the step distillation in an unpredictable way. A de-distill training adapter slows this process down significantly allowing you to do short training runs while preserving the step distillation (speed). You could also train directly on the raw model, but training directly on the turbo model could yield better results. This is really just a hack to significantly slow down the distillation when fine-tuning a distilled model. The distillation will still be broken down over time. What that means is, this adapter will work great for shorter runs such as styles, concepts, and characters. However, doing a long training run will likely lead to the distillation breaking down to a point where artifacts will be produced when the adapter is removed.
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查看详情 ↗OpenFLUX.1
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查看详情 ↗sketch_to_image_klein_4b
ostris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 ↗Z-Image-De-Turbo
ostrisThis is a de-distilled version of Tongyi-MAI/Z-Image-Turbo. It was fine tuned on images generated by Tongyi-MAI/Z-Image-Turbo in order to break down the turbo distillation. It can be used to train LoRAs on top of that should remain compatability with the base model, or it can be continued to finetune well beyond what you could do with the turbo model with the ostris/zimage_turbo_training_adapter. This can be trained on directly without an adapter. It can also be used for inference directly. For inference, use a low CFG (2.0 - 3.0) and 20-30 steps. It seems to work well with CFG normalization as well.
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查看详情 ↗This is a training adapter designed to be used for fine-tuning Tongyi-MAI/Z-Image-Turbo. It was made for use with AI Toolkit but could potentially be used in other trainers as well. If you are implementing it into training code and have questions. I am always heppy to help. Just reach out. It can also be used as a general de-distillation LoRA for inference to remove the "Turbo" from "Z-Image-Turbo". When you train directly on a step distilled model, the distillation breaks down very quickly. This results in losing the step distillation in an unpredictable way. A de-distill training adapter slows this process down significantly allowing you to do short training runs while preserving the step distillation (speed). This is really just a hack to significantly slow down the distillation when fine-tuning a distilled model. The distillation will still be broken down over time. What that means is, this adapter will work great for shorter runs such as styles, concepts, and characters. However, doing a long training run will likely lead to the distillation breaking down to a point where artifacts will be produced when the adapter is removed.
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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。