CSGO
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 11
FLUX.1-dev-Controlnet-Canny
InstantXWe have completed the training of the first version. The training was conducted with a total pixel count of `1024*1024` at multi-scale. We trained for 30k steps using a batch size of 8*8.
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查看详情 ↗FLUX.1-dev-Controlnet-Union
InstantXThe training of union controlnet requires a significant amount of computational power. The current release is the first beta version checkpoint that maybe not been fully trained. The fully trainedbeta version is in the training process. We have conducted ablation studies that have demonstrated the validity of the code. The open-source release of the first beta version is solely to facilitate the rapid growth of the open-source community and the Flux ecosystem; it is common to encounter bad cases (please accept my apologies). It is worth noting that we have found that even a fully trained Union model may not perform as well as specialized models, such as pose control. However, as training progresses, the performance of the Union model will continue to approach that of specialized models.
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查看详情 ↗FLUX.1-dev-IP-Adapter
InstantXThis repository contains a IP-Adapter for FLUX.1-dev model released by researchers from InstantX Team, where image work just like text, so it may not be responsive or interfere with other text, but we do hope you enjoy this model, have fun and share your creative works with us on Twitter. This is a regular IP-Adapter, where the new layers are added into 38 single and 19 double blocks. We use google/siglip-so400m-patch14-384 to encode image for its superior performance, and adopt a simple MLPProjModel of 2 linear layers to project. The image token number is set to 128. The currently released model is trained on the 10M open source dataset with a batch size of 128 and 80K training steps. This model supports image reference, but is not for fine-grained style transfer or character consistency, which means that there exists a trade-off between content leakage and style transfer. We don't find similar properties in FLUX.1-dev (DiT-based) as in InstantStyle (UNet-based). It may take several attempts to get satisfied results. Furthermore, current released model may suffer from limited diversity, thus cannot cover some styles or concepts,
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查看详情 ↗InstantID
InstantXInstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. You can directly download the model in this repository. You also can download the model in python script: For face encoder, you need to manutally download via this URL to `models/antelopev2`.
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查看详情 ↗MiniMax-H3-Turbo-Lora-Diffusers
InstantXDiffusers / PEFT conversion of `larryvrh/MiniMax-H3-Turbo-Lora`: a LoRA that lets MiniMax-H3 render joint **video + synchronized stereo audio** in about **4 sampling steps** instead of the usual ~20. > ⚠️ **Early prototype.** Same caveat as the upstream release: under-trained preview weights, not production quality. They already beat the base model at 4 steps (sharper detail, cleaner / better-synced audio), but treat this as a work-in-progress taste, not a finished product. Prefer the non-EMA `ckpt500` weights by default. Converted from the upstream Turbo LoRA (bf16, `W_eff = W + lora_B @ lora_A`, **alpha = rank** so scale is 1). QKV is split into `to_q` / `to_k` / `to_v`, and SwiGLU `fc1` halves are swapped to match Diffusers' `[value; gate]` layout.
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查看详情 ↗Qwen-Image-ControlNet-Inpainting
InstantXThis repository provides a ControlNet that supports mask-based image inpainting and outpainting for Qwen-Image. ComfyUI offers native support for Qwen-Image-ControlNet-Inpainting. The official workflow can be found here. Make sure your ComfyUI version is >=0.3.59. Liblib AI offers native support for Qwen-Image-ControlNet-Inpainting. Visit for online WebUI or ComfyUI inference.
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查看详情 ↗Qwen-Image-ControlNet-Union
InstantXThis repository provides a unified ControlNet that supports 4 common control types (canny, soft edge, depth, pose) for Qwen-Image. You can adjust control strength via controlnet_conditioning_scale. - Canny: use cv2.Canny, set controlnet_conditioning_scale in [0.8, 1.0] - Soft Edge: use AnylineDetector, set controlnet_conditioning_scale in [0.8, 1.0] - Depth: use depth-anything, set controlnet_conditioning_scale in [0.8, 1.0] - Pose: use DWPose, set controlnet_conditioning_scale in [0.8, 1.0] We strongly recommend using detailed prompts, especially when include text elements. For example, use "a poster with text 'InstantX Team' on the top" instead of "a poster".
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查看详情 ↗SD3.5-Large-IP-Adapter
InstantX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 ↗SD3-Controlnet-Canny
InstantX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 ↗SD3-Controlnet-Pose
InstantX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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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。