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    Krea 2 Turbo Official Comfy-Org Checkpoints (Krea2) - krea2_turbo_fp8
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    Model Overview

    • Model Name: Krea 2

    • Version: v1.0

    • Release Date: June 22, 2026

    • Model Type: Text-to-image diffusion model

    • Architecture: Diffusion Transformer with 12 billion parameters

    • License: Krea 2 Community License

    • Release Format: Open-weight release and Krea-hosted product integrations

    • Model Developer: Krea.ai, Inc.

    Quantization Matrix

    same seed/prompt comparison

    • FP16 (Half Precision)

      • Element Size: 16-bit (2 bytes)

      • Storage Size: 100% (Baseline)

      • Accuracy Retention: 100%

      • Target Cards: All GPUs (Native)

      • RTX 3000/4000 Series: Runs natively out of the box with zero conversion overhead.

    • FP8 (Standard)

      • Element Size: 8-bit (1 byte)

      • Storage Size: ~50%

      • Accuracy Retention: ~99.5% to 99.9%

      • Target Cards: Ada Lovelace (RTX 4000)

      • RTX 3000/4000 Series: Requires software upcasting on 3000 series, causing minor speed drops.

    • INT8_convrot (INT8 Convolutional Rotation)

      • Element Size: 8-bit (1 byte)

      • Storage Size: ~50%

      • Accuracy Retention: Near-lossless (~99.8% to 100%). Ranks just below GGUF Q8 but generally outperforms standard FP8 and MXFP8 by rotating weights and activations to suppress outliers.

      • Target Cards: Any NVIDIA GPU with INT8 Tensor Cores (RTX 3000 series and newer).

      • RTX 3000/4000 Series: Runs natively with full hardware acceleration on both. Highly advantageous for RTX 3000 (Ampere) cards, which lack FP8 tensor cores but feature dedicated INT8 pipelines, entirely bypassing the FP8 software upcasting penalty for faster generation.

    • MXFP8 (OCP Microscaling)

      • Element Size: 8-bit + 32-block scale

      • Storage Size: ~50% + scale overhead

      • Accuracy Retention: ~99.8% to 100%

      • Target Cards: Blackwell (RTX 5000)

      • RTX 3000/4000 Series: Runs via software scaling layers; expect slower speeds due to legacy hardware limitations.

    • NVFP4 (NVIDIA 4-Bit)

      • Element Size: 4-bit + 16-block scale

      • Storage Size: ~25% to 28%

      • Accuracy Retention: ~99.0% (Within 1% of baseline)

      • Target Cards: Blackwell (RTX 5000)

      • RTX 3000/4000 Series: Runs via software emulation (ModelOpt/TRT-LLM); generations are slower without native Blackwell block-math pipelines.

    Model Family and Release Checkpoints

    This model card covers the Krea 2 model family, including the following release checkpoints:

    • Krea 2 Raw: Base release checkpoint, prior to additional post-training and fine-tuning.

    • Krea 2 Turbo: Post-trained release checkpoint with additional fine-tuning and distillation.

    Capabilities and Intended use

    Krea 2 is a text-to-image diffusion model that generates images from natural-language text descriptions. The model is designed to support creative, commercial, developer, and research use cases, including image generation, concepting, design exploration, visual production workflows, and integration into applications and creative tools.

    Out-of-Scope Uses

    This model is not intended or designed for uses that violate applicable law or regulations, infringe or misappropriate third-party rights, generate or facilitate unlawful or harmful content (including CSAM, NCII, harassment or defamation), or support fully automated decision-making that adversely affects legal rights of individuals. This summary is non-exhaustive. Use of Krea 2 is subject to the Krea 2 Community License Agreement and must comply with the Acceptable Use Policy. In the event of any conflict, the Krea Acceptable Use Policy and Krea 2 Community License control.

    Training Data

    This model was developed using a combination of publicly available data, data licensed from third-party providers, and synthetic data generated through proprietary methods. The training data includes images and their associated captions or text descriptions.

    Prior to training, data was filtered to remove certain categories of harmful content and reduce low-quality, duplicative, or irrelevant data. Krea also used curated and synthetic training data selected to improve prompt following, visual quality, and alignment with intended use cases.

    Safety Measures

    We implemented safety measures across the full model development lifecycle. We applied targeted fine-tuning techniques to reduce the model's susceptibility to generating harmful content in response to both direct and adversarial prompts, and we conducted multiple rounds of internal and external safety evaluation before release.

    For Krea's hosted products incorporating Krea 2, we deploy input and output classifiers using a combination of proprietary and third-party detection tools to flag or block policy-violating prompts and generated images.

    Because this is also an open-weights release, Krea does not control downstream deployment of the model. Under the Krea 2 Community License, deployers are required to implement content filtering measures or equivalent review processes to prevent the generation or distribution of unlawful or policy-violating content appropriate to their use case. Deployers who fail to implement required safeguards are in breach of the license. See the license for details.

    We conducted multiple rounds of internal and external safety evaluations before release, including adversarial testing designed to assess the model's resilience to attempts to elicit harmful or policy-violating outputs. Testing covered sexually explicit content, non-consensual intimate imagery, child-safety risks, and other high-risk content categories. Based on these evaluations, the release checkpoints demonstrated high resilience against violative inputs across the tested risk categories.

    Krea maintains reporting channels for harmful, illegal, or policy-violating outputs at [email protected]. Reports involving potential CSAM are escalated to NCMEC as required by law. Krea reserves the right to update model weights or revoke access in response to identified misuse patterns.

    Risks and Limitations

    Krea 2 is a new technology and there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all possible scenarios. The model's potential outputs cannot be predicted in advance and may, in some instances, produce inaccurate, objectionable, or otherwise undesirable outputs.

    This model is not intended to provide factual information. The model may fail to generate output that matches the prompt, and prompt following may be influenced by prompt style, specificity, language, and phrasing.

    Before deploying any application using this model, developers should perform safety testing and tuning tailored to their specific application and must implement safeguards required by the Krea 2 Community License.

    License and Outputs

    Krea does not claim copyright or other intellectual property rights over content generated by users of this model. Users are solely responsible for their outputs and any subsequent use of those outputs. As with other generative tools, the nature of a user's inputs influences the outputs produced, and prompts may produce images that implicate third-party rights. Users are solely responsible for assessing and addressing those risks. See the Krea 2 Community License for more information.

    Krea 2 is licensed under the Krea 2 Community License Agreement. For more information, visit https://krea.ai/krea-2-licensing.

    Description

    krea2_turbo_fp8 - This is the official Comfy Krea 2 Turbo FP8 checkpoint. Uses ~50% VRAM compared to BF16. Best on RTX 3000/4000 series rigs. Source: https://huggingface.co/Comfy-Org/Krea-2

    FAQ

    Comments (19)

    kreslJun 24, 2026
    CivitAI

    are there any image comparison between the models?

    daceheg192491
    Author
    Jun 24, 2026· 3 reactions

    Quality is going to be pretty similar. The data in the main post compares them. It's more about which GPU and how much VRAM you have to burn.

    daceheg192491
    Author
    Jun 25, 2026· 4 reactions

    Here's a same seed / same prompt comparison https://civitai.red/posts/29420066

    bnx005514Jun 27, 2026· 1 reaction
    CivitAI

    I am using a amd 7900xtx and i wonder which model i should choose. Only the fp16 one?

    daceheg192491
    Author
    Jun 27, 2026· 1 reaction

    bf16 would likely be the choice I think for you.

    gausssidorov928Jun 28, 2026

    для генерации nvfp4

    gausssidorov928Jun 28, 2026· 1 reaction
    CivitAI

    Лучшая модель которая отправит на пенсию SDXL, для генерации используйте NVFP4 , для обучения LORA только FP16

    RenesiJun 28, 2026

    Она отправит SDXL на пенсию только при условии, если будет такой-же быстрой, гибкой, адаптивной, и без цензуры.
    Но если все эти критерии действительно будут соблюдены, и (главное) удобны в использовании, тогда АБСОЛЮТНО ДА! :)

    aiAnaistaJun 28, 2026

    Да, но NVFP4 только если у тебя карта RTX 5000 серии?

    daceheg192491
    Author
    Jun 28, 2026

    @aiAnaista Форматы NVFP4 и MXFP8 предназначены для карт Blackwell (серии 5000); в противном случае их быстродействие будет ниже. (google translated)

    RenesiJun 28, 2026· 1 reaction
    CivitAI

    Which version is better to use on RTX3090? I read a description, and it caused more questions, than answers :D

    And also, 24GB size... It it even gonna run on RTX 3090?

    FloatsYourStoatJun 28, 2026· 1 reaction

    If you are using comfy it's great at memory management, if I were in your shoes I'd try the bf16. (edited to remove the fp8 recommendation for speed, apparently not accelerated on 30 series, may as well try mxfp8 as a fall back if you are going to lose acceleration anyways)

    daceheg192491
    Author
    Jun 28, 2026· 1 reaction

    BF16 or FP8 (less vram, slightly slower) should work

    aiAnaistaJun 28, 2026

    I use FP8 on my 3090, but with high-res and adetailer in Comfy it takes a very long time per picture (500 seconds on average).

    daceheg192491
    Author
    Jun 28, 2026

    @aiAnaista Worth trying the BF16 to compare? If you do please report back

    aiAnaistaJun 28, 2026

    @daceheg192491 I tried krea2_turbo_fp8 and krea2_turbo_nvfp4.
    krea2_turbo_fp8 generates picture in ~65 seconds.
    krea2_turbo_nvfp4 generates picture in ~35 seconds.
    As you can see, the second one almost is twice as fast with my specs (RTX3090 24GB + 32GB DDR4) which pleasantly surprised me.
    I used Lonecat's Simple Workflow for KREA 2 for this test.

    daceheg192491
    Author
    Jun 28, 2026· 1 reaction

    @aiAnaista NICE! That's really good. The smaller file size must be helping quite a bit which is impressive.

    elevendrJun 28, 2026· 4 reactions
    CivitAI

    If somebody can make a LoRA to make the model be able to accept multi image inputs for editing and style transfer, i will bless you for everything

    MrSanaJun 29, 2026
    CivitAI

    Based on my initial experiences, Model is generally good. I'm running it with 16GB of VRAM and it's fast enough. I normally use Illustrious as my base model. If I compare the two:

    Background creation is quite good. Backgrounds are detailed and generally accurate.

    The accuracy in perspective and body proportions is not bad.

    Applying different styles isn't bad. However, only general styles work.

    It can recognize popular characters, but not sufficiently. Illustrious is much better at this.

    NSFW filter is strong. It only allows safe content.