REDKREA2-Alternating Evaluation
REDGPT2-Turbo-逼真剧创

The Grand Pussy Truth 普世真理

KREA2 GPT2 逼真剧创 Alternating Evaluation | MIST3 9/2/2026

采用 SDA（语义方向对齐）训练——基于教师指导的多样性对齐损失，结合 Forward XM 最佳-5候选者探索方法，在单节点高噪声（σ = 0.9567）上完成训练。本方案已取得 @fok3827 商业使用授权，采用双模型高低区 sigmas（特指降噪参数序列）交叉去噪，4H+6L 交叉。

本模型隶属于 AiMetatron Paid Access 模型服务包，所有购买了红潮/黑兽 H3 服务的用户可以找我获取授权下载地址。单独在本站购买 REDKREA2 仅获得本页面模型的Access。

免费商业使用的条件

Krea 2 是 Krea.ai 公司发布的 AI 图像生成模型，提供两个可下载变体：Krea 2 Raw 和 Krea 2 Turbo。其商业使用受 Krea 2 Community License Agreement（社区许可协议）约束。在社区许可协议下，免费商业使用 的前提是：你（包括所有共同所有或控制的关联实体）的全公司年总收入低于 100 万美元（$1,000,000 USD），按过去 12 个月滚动计算。

输出的所有权

你拥有你生成的输出（Outputs），前提是你遵守本协议。
Krea 对输出不主张所有权。
你需对输出的内容、准确性、合法性以及使用或分发的一切后果承担全部责任。

KREA 2 社区许可协议全文阅读

Trained with SDA (Semantic Directional Alignment) — a teacher-guided diversity alignment loss — wrapped in Forward XM best-of-5 candidate exploration, on a single high-noise sigma node (σ = 0.9567). This method is commercially licensed by @fok3827 . It employs two models with dedicated high-noise and low-noise sigma schedules (i.e., denoising parameter sequences), alternating between them for cross-denoising in a 4H + 6L configuration.

This model belongs to the AiMetatron Paid Access model service package. All users who have purchased the RedCraft/Dark Beast H3 service can contact me to obtain an authorized download address. Purchasing REDKREA2 separately on this page will only provide access to the REDGPT2 Krea2 fine-tune files.

Conditions for Free Commercial Use

Krea 2 is an AI image generation model released by krea.ai, available in two downloadable variants: Krea 2 Raw and Krea 2 Turbo. Its commercial use is governed by the Krea 2 Community License Agreement.Under the Community License Agreement, free commercial use is permitted only if your total company-wide annual revenue (including all affiliated entities under common ownership or control) is less than one million US dollars ($1,000,000 USD), calculated on a trailing twelve-month basis.

Ownership of Outputs

You own the Outputs you generate, provided that you comply with this Agreement.

Krea claims no ownership of Outputs.

You are solely and exclusively responsible for the content, accuracy, legality, and all consequences of the use or distribution of the Outputs.

KREA 2 COMMUNITY LICENSE AGREEMENT

这个版本的 Krea2 Fine-Tune 由高噪权重和低噪权重两个文件组成，并通过 “Alternating Evaluation” 方案进行交叉采样。
This Krea2 Fine-Tune consists of two separate weight files: a high-noise model and a low-noise model. They are used in an “Alternating Evaluation” sampling scheme, where the two models are interleaved throughout the generation process.

The goal of this approach is to incorporate the high-noise-region diversity recovered from Krea2 RAW in @fok3827 ’s SDA (Semantic Directional Alignment) training results. To better preserve the details obtained through diversity-oriented sampling, the two models are not simply used in a sequential handoff. Instead, they are alternated according to the original training concept, helping prevent useful variations introduced in the high-noise region from being lost during subsequent inference steps.

@fok3827 treats Semantic Directional Alignment treats diversity collapse as a direction problem. For one training image x0 we draw two noises (z1, z2) and noisify both to σ = 0.9567 — the highest learnable step of the 8-step Turbo schedule, where composition is decided. The frozen teacher (Krea 2 RAW, the non-distilled parent) and the student (Turbo + LoRA) each predict x0 for both noises; both predictions are decoded and embedded by a frozen CLIP stack. The teacher's feature delta ΔT records which direction in perceptual space this noise swap should move the image; the loss L_div = 1 − cos(ΔS, ΔT) teaches the student's delta ΔS to point the same way instead of collapsing all noises onto one template. An SFT self-anchor keeps the student's own trajectory stable.

It is important to note that restoring diversity generally comes with a certain trade-off in generation stability and may increase the probability of artifacts such as anatomical or limb-related errors. Therefore, this approach is not intended to maximize randomness indiscriminately. Instead, dual-model alternating sampling is designed to produce richer and more personalized expressions of cinematic and drama-oriented visual elements, reduce the repetitive “one-style-fits-all” look often seen in generative outputs, and recover more of the stochastic sampling behavior characteristic of DiT-based Flow Matching models.

The ultimate goal is to bring back the creative unpredictability of Flow Matching sampling while maintaining a usable level of stability, allowing the model to produce more diverse, less predictable, and more original visual details.
需要注意的是，恢复多样性通常会以一定程度的生成稳定性作为代价，并可能增加肢体结构等问题出现的概率。本方案目的在于通过双模型交替采样来获得影视剧主题元素的更丰富个性化表达方式，减少千人一面的作品风格，还原 Dit flowmatch 模型抽卡的乐趣，表现出更多的原创性。

其设计目的是借鉴 @Fok 在 SDA（Semantic Directional Alignment） 训练中，基于 Krea2 RAW 所恢复的高噪区域多样性。为了更好地保留通过多样性采样获得的细节，本方案并非让两个模型进行简单的阶段接力，而是根据原作者的训练思路，在采样过程中交错使用高噪与低噪权重，从而尽可能避免高噪区域中的推理结果在后续过程中被抹平或丢失。

女性尿道口随机抽卡多样性比较。

Compare the random sampling of female urethral meatus via Krea2-REDGTA2 and Krea2-RedCraft.

Dark Beast | 黑兽 H3 Director Edition 自动短剧生产线 08/28/2026
https://civarchive.com/models/2242173/dark-beast-or-h3-director-edition
是原生H3单次采样直出2k分辨率的高清方案（ 768P/VSR/RIFE TensorRT ）

RedCraft | 红潮 | REDMIX Hybrid A2A beta2 + LTX25 2k 16-bit HDR- 08/25/2026
https://civarchive.com/models/958009/redcraft-or-or-hybrid-h3-a2a-beta2-ltx25-2k
beta2 版本融合了 LTX2.5 distilled 作为2k高清化方案，并且消除了音频bug。

REDGraft | LTX 2.5 | 老同学 Fast 2K 16bit-HD| sulphur2 ported 移植版 08/21/2026
https://civarchive.com/models/1295569/redgraft-ltx-25-fast-2k-or-sulphur2-ported
LTX 16 bit HDR & Nvidia RTX VSR

以上付费用户提供一对一配置服务

One-on-one configuration services are provided to paid users.

本地 A5000 24G 显存设备，输出2k 8秒视频≈360秒

商用 5090D 24G 显存设备，输出2k 8秒视频≈240秒

红潮/黑兽/LTX老同学系列模型即将登录 Fal.ai 平台

届时可以体验 B200 飞速生产的效果（预计40-60秒）

红潮 / 黑兽模型在线体验预览 https://asop.uk | https://app.asop.uk

The "RedCraft" "Black Beast" and "REDGraft LTX" model series are coming soon to the Fal.ai .
Will be the lightning-fast generation speeds of the B200 (estimated at 40–60 seconds).
RedCraft / Dark Beast – Online Model Preview https://asop.uk | https://app.asop.uk

No Mosaics. 无码影视专用

KREA2 GPT 逼真版 Grand PUSSY Truth | MIST2 7/13/2026

文生图版：RedCraft | 红潮 | KREA 2 赤佬2 Bastard Edition （INT8/INT4）

硬核版本：Dark Beast | 黑兽 🐱‍👤Krea2赤佬无码版 已发布 06/28/2026

MIST XL Character Style Model 角色风格模型 AiARTiST

训练底模使用的是：Pony Diffusion V6 XL

直达链接： Pony Diffusion V6 XL - V6 (start with this one)

模型清单： https://www.liblib.art/search?keyword=AiARTiST

开源模型 UNIT-lib XL（已包含1GIRL权重，叠加使用可加强人像）：

AiARTiST XL 基础单元 CADS2 LoRA 兼容版境内链接

https://www.liblib.art/modelinfo/b10dfccc06f34dfa9031f1d070d846ee

UNIT-lib XL 开放会员下载，支持融合，会员同时享有在线加速生成

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内置加速器 Accelerator：Hyper-SDXL | 快过闪电的设计渲染

Hyper-SD是最新扩散模型加速技术之一，无损高清加速，CN适配良好

经过多个底模的实测，Hyper-SD可推导出比加速前更多的画面信息！

Hyper-SD is one of the latest diffusion model acceleration technologies, with lossless high-definition acceleration and good CN adaptation.

After actual measurements on multiple base models, Hyper-SD can derive more screen information than before acceleration!

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触发词 Triger Word：1GIRL,Any_Girl

生成参数参考：

parameters

This photo shows a chinese woman wearing a warm and textured sweater,holding a steaming cup in her hand. She sat in the dimly lit room,with soft golden lights illuminating her side. The lady is braiding her hair,seemingly fully focused on this moment,perhaps savoring the aroma of the drink. The background has a rural feel,with a plant in the vase and textured wall

Negative prompt: NSFW

Steps: 10, Sampler: Euler a, CFG scale: 2, Seed: 3573086999, Face restoration: CodeFormer, Size: 640x1280, Model hash: db0525a2bc, Model: AiARTiST-MIST-HYPER.fp16, Denoising strength: 0.4, ADetailer model: face_yolov8n.pt, ADetailer confidence: 0.3, ADetailer dilate erode: 4, ADetailer mask blur: 4, ADetailer denoising strength: 0.4, ADetailer inpaint only masked: True, ADetailer inpaint padding: 32, ADetailer version: 23.11.1, Hires upscale: 1.5, Hires upscaler: Latent, kohya_hrfix_enabled: True, kohya_hrfix_block_number: 3, kohya_hrfix_downscale_factor: 2, kohya_hrfix_start_percent: 0, kohya_hrfix_end_percent: 0.35, kohya_hrfix_downscale_after_skip: True, kohya_hrfix_downscale_method: bicubic, kohya_hrfix_upscale_method: bicubic, Mask blur: 4, Inpaint area: Only masked, Masked area padding: 32, Version: f0.0.17v1.8.0rc-latest-276-g29be1da7

后期处理

Postprocess upscale by: 2, Postprocess upscaler: R-ESRGAN 4x+

It is recommended to use the sample picture prompt words directly, and then add modified detail words as needed.

「建议直接使用样图提示词，然后根据需要增添修改细节词」

推荐采样步数：10 CFG scale: 1，VAE Automatic 内置VAE，Clip Skip：1

推荐采样方法：Euler A，DDIM，DPM++ 2M Turbo

Hyper sample steps：10，CFG scale: 2，Clip Skip 1

Hyper sampler：Euler A

推荐分辨率：任意分辨率

2 Steps：Text2img long side 1280，Send to img2img 0.4-0.5 resize x1.5

如果图片尺寸用于出版，可发送到「Tiled Diffusion」或「后期处理」扩大

图片后期处理高清化设置： 8x_NMKD-Superscale_150000_G 可叠加

4x-UltraSharp 或 R-ESRGAN 4x+ Anime6B

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More words

As of 2024, there are still many friends who don’t know much about XL. They think that online image generation is slow and that you need to record tags when publishing images.

In fact, these are concepts left over from outdated tutorials. First of all, I tested the online drawing today and found that the drawing speeds of XL and 1.5 are almost the same.

Secondly, XL benefits from a larger number of parameters and a double text layer design. In fact, it can generate most drawing styles based on the base model color.

Even if some concepts are locked through LoRA, the required screen elements can be changed arbitrarily through prompt words. There is also an automatic translation tool in lib online.

SDXL is a form between the commercial model and the 1.5 community model. It does not require too many LoRA combinations or complex prompt words.Negative words, all you need is your creativity and aesthetic taste. Even by redrawing from graph to graph, you can get very good results.

In addition, I always hear a voice saying that XL's ControlNet model is not easy to use. As of May 2024, the SDXL ecosystem has more than 20 mature CN models, and the number is still increasing. 

Soft and hard edges, Openpose, depth and normal maps, line drawings, straight lines, graffiti, various face-changing plug-ins, and high-definition redrawing are all available. 

For problems such as artifacts and image quality degradation that are prone to occur in XL's CN controller model, it can be optimized by appropriately reducing CFG and increasing the number of steps.

后话

截止到2024年，还是有很多朋友对XL不是很了解，以为在线生图慢，出图需要记标签。

其实这些都是过时教程遗留下来的观念，首先今天我实测了Lib在线生图，XL和1.5出图速度相差无几。

其次，XL得益于较大的参数量和双文本层设计，其实根据底模特色，自身已经可以生成绝大多数绘图风格。即便是通过LoRA锁定了部分概念，也可以通过提示词任意变更所需的画面元素，lib在线还有自动翻译工具。SDXL就是介于商业模型和1.5社区模型之间的一种形态，不需要过多的LoRA组合，也不需要复杂的提示词、负面词，需要的只是你的创意和审美品味。即便是通过图生图重绘，也可以获得非常好的结果。

另外，还总听到一种声音，说XL的ControlNet模型不好用什么的。截至2024年5月，SDXL生态已经有20多款成熟的CN模型，数量还在不断增加。软硬边缘、Openpose、深度与法线贴图、线稿、直线、涂鸦、各种换脸插件、高清重绘一应俱全。对于XL的CN控制器模型容易出现的伪影，画质下降等问题，可以通过适当降低CFG，提高步数来优化。

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模型用途声明：

1. 您不得将此模型及其衍生版本（如融合模型版本）托管于计划赚取收入或捐赠的网站/应用程序。

2. 您不得直接售卖此模型及其衍生版本（如融合模型版本），除非您对此模型进行了足够程度的人工修改，使其在法律意义上可以被完全判定为您的个人作品。如果您违反本条，所造成的一切法律后果由您个人承担，请恕本人概不负责。

3. 您不能使用该模型故意制作或共享非法或有害的内容传播和输出，请您遵守公序良德，将此模型用于积极正面的用途。

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境内推荐SDXL Hyper加速模型：AiARTiST-CADS2.0 XL 商业广告辅助系统（企业定制）

直达链接： https://www.liblib.art/modelinfo/a56ebacdba7d4e30b97bb124bc3fc28f

模型清单： https://www.liblib.art/search?keyword=AiARTiST

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