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These are some XL models based on NoobAI/pony XL with a little modifications, they are mainly for anime/realistic bara kemono content, and should be ok for all contents. I chose pony purely because of its NSFW ability. (Now I'm thinking about switching to NoobXL because Pony is a pain in the ass to tweak) NoobAI based model out now. If you like my work, consider buy me a coffee please <3 (completely voluntary) \u5982\u540cLicense\u5185\u63d0\u53ca\u7684\uff0c\u672c\u6a21\u578b\u53ca\u5176\u751f\u6210\u7684\u56fe\u50cf\u7981\u6b62\u5546\u4e1a\u5316\u4f7f\u7528\uff01 For CkNoob_EPS_\u216a_11: This is a new EPS model that based on Chenkin Noob v0.1. Based on my testing, compared to EPS10, this model features better background coherence, higher contrast, slightly more detail, and a bit more responsiveness to artist styles. Just note that the composition of the generated images with the same prompt can sometimes differ noticeably from EPS10. And the overall feel and behavior of this model can sometimes differ from EPS10. Just as the EPS10, when using this model, try using a CFG scale between 3\u20137 (recommended 5). It is recommended to keep using Euler a sampler for best overall results, but switching to DPM++ 2M may produce more detailed (and chaotic) outputs. For NoobAI_EPS_\u2169_10: This is a new EPS model that merges the the core features (hopefully) of EPS3 and EPS7. It aims to increase overall versatility while sacrificing just bit of fine detail. Backgrounds are much more stable compared to EPS3, though occasionally not as consistent as EPS7. EPS10 fixed the issue of overly saturated or blown-out colors at high CFG values. Artist tags still work well as before, and stability when using the DPM++ 2M sampler has been improved. When using this model, try using a CFG scale between 3\u20137 (recommended 5). It is recommended to keep using Euler a sampler for best overall results, but switching to DPM++ 2M may produce more detailed (chaotic) outputs.

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This is a dedicated rank-64 LoRA trained with one narrow objective: reduce Krea2\u2019s learned refusal and restriction behavior while preserving as much of the base model\u2019s existing visual knowledge and prompt behavior as possible. This is not a concept, character, style, anatomy, or aesthetic LoRA. It was not trained to add new visual concepts, reproduce a training-image set, or impose a preferred look. Its training objective was exclusively to push the model away from refusal behavior so that concepts already represented within the base model are less likely to be suppressed during text conditioning. Krea2 receives multiple hidden-state taps from its Qwen-VL text encoder and processes them through TextFusion before sending the resulting conditioning into the image transformer. This LoRA applies learned low-rank residuals only to the attention and internal MLP projections within: - txtfusion.layerwise_blocks.0 - txtfusion.layerwise_blocks.1 - txtfusion.refiner_blocks.0 - txtfusion.refiner_blocks.1 The release version deliberately contains no adapter for: - The TextFusion 1 \u00d7 12 tap projector - The external/general txtmlp - The image-transformer blocks or any other image-generation layers The base checkpoint is never overwritten. For every targeted linear layer, LoRA adds a learned rank-64 residual to the original weight during inference: W_effective = W_base + strength \u00d7 \u0394W This changes how existing Qwen-VL text features are routed, gated, transported, and refined through TextFusion before they reach Krea2\u2019s untouched image transformer. In other words, the LoRA is intended to improve access to visual knowledge already present in the base model, not inject a new concept or replace the model\u2019s learned visual representations. This release targets the specific TextFusion route isolated through layer-by-layer ablation instead of broadly amplifying activations or directly alte...

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UPDATED 8/22/2026 - Announcing Krea 2 Turbo Edit v1.1 GGUF and FP8 an all new updated version with bug fixes and major improvements. 1. **Fine Detail LoRA** - Sharpens and refines facial features and "sensitive" parts. (0.75 strength.) 2. **Identity Edit LoRA** - Strong edits and identity preservation. (1.0 strength) 3. **4-step Turbo LoRA** - The latest 4-step rank-64 lora that reduces the minimum usable step count from 8 to 4. (1.0 strength) 4. **Skin Detail LoRA** - Skin refiner to remove the "plastic" look (0.45 strength) 5. **Refusal Reduction NSFW LoRA** - The latest NSFW rank-64 lora to supress restrictions. (0.8 strength) Original Prompt: Edit this picture. She is wearing a bikini only. Keep the shirt on the bunny that says "Krea2". Soft studio lighting, white background, 3/4 body shot.

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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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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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