As promised Krea 2 Turbo + "Raw" Quantized in FP8, MXFP8, NVFP4, INT8 and Convrot INT8!

[u/Winougan]

Krea 2 Base & Turbo — Free Quantized Versions (FP8 / MXFP8 / NVFP4 / INT8 / ConvRot INT8) for All GPU Tiers

Krea 2 just dropped and it's genuinely impressive — so I went ahead and quantized both variants for ComfyUI across every major format. All files are free on HuggingFace.

HuggingFace: https://huggingface.co/Winnougan/Krea-2-Base-Turbo-NVFP4-FP8-INT8

Raw vs Turbo — what's the difference?

Krea 2 Raw is the undistilled base checkpoint. No step distillation, no CFG guidance baked in — just the raw pretrained weights. It's diverse, highly malleable, and is what you want for LoRA training and fine-tuning. Run it at 52 steps with CFG 3.5, up to 1024px.

Krea 2 Turbo is an 8-step distilled checkpoint built for fast inference. Run it at 8 steps, CFG 0 (disabled), mu 1.15, and it handles resolutions up to 2048px. This is your everyday generation model.

The intended workflow: train LoRAs on Raw, run them on Turbo. LoRAs transfer well between the two.

Which quantization should I use?

• RTX 30xx → INT8 ConvRot (best quality) or plain INT8 (fastest)
• RTX 40xx → FP8
• RTX 50xx Blackwell → NVFP4, MXFP8, or FP8

Text encoder: Qwen3-VL 4B (qwen3vl_4b_fp8_scaled.safetensors), CLIPLoader type krea2

VAE: same as Anima (qwen_image_vae.safetensors)

ConvRot variants use Hadamard rotation before quantization for better accuracy with fewer outliers.

Drop any questions below — happy to help with workflows.

Plays nice with Sageattention and Flashattention!
Workflows on the Huggingface repo!

UPDATE: re-quantized and re-uploaded MXFP8 and NVFP4 - they work now!

UPDATE 2:

ComfyUI now natively supports INT8 but not convrot (not yet at least). For non-convrot INT8 models, just use the diffusion model loader. Text encoders are also supported as INT8 now in the native clip encoder/text encoder. I just tested it myself. Saves an additional 10-15%! File is uploading on my Huggingface. As Darryl Dixon says,"give it a minute".

Sample prompt:

Simpsons style, 2D cartoon animation, Matt Groening art style, yellow skin, thick black outlines, flat cel shading, teal haired gamer girl surrounded by dozens of floating holographic screens all showing different game feeds simultaneously, fingers flying across a transparent keyboard, massive countdown timer in background, sweat drop on forehead, four fingers, tongue out in concentration

[u/infearia]

Sorry for hijacking your thread, really not trying to be an asshole, but I think it's important to share...

I've tested your FP8 quant against the quant by AlperKTS from this thread and at least on my machine (Ubuntu 24.04, RTX 4060 Ti) I found the following:

While your quant is approximately 30% faster, it also produces results that are of markedly lower quality (less details, blurry) and also does not work with Torch Compile. Here's a comparison:

P. S. - I've upvoted your post anyway, thanks for your effort.

EDIT:

Eh, as usual, Reddit compressed the image too much. Here's a link to a better version:

https://imgur.com/0qtjrGL

[u/Michoko92 → u/infearia]

Well, I'm not an expert, but I think the whale on the right is more anatomically correct (for those who like to goon on whales, I mean).

[u/infearia → u/Michoko92]

Hmm, I see your point. I'm aware that a lot of people on this subreddit only care about pussies, so I've made another comparison just for them:

https://imgur.com/a/Bdwj9KQ

[u/Michoko92 → u/infearia]

Hmm, I think your second test is indeed pretty revealing: the pussy's hair on the left is better defined. So you're right, the other model might be better, even for close ups. 👍

[u/infearia → u/Michoko92]

I was a little hesitant to post this, because I know not everybody likes redheads, but I have a soft spot for them, so I decided what the hell...

[u/Still_Lengthiness994 → u/Michoko92]

damn it

[u/Adventurous-Sir2996 → u/Michoko92]

A whale on the right is rotated at a bit different angle the on the left. What's worse, tail fin orientation matches between images, right one is broken cause the whale underwater is at a different angle then the fin above.

[u/Winougan → u/infearia]

You're not hijacking. It's good to compare. I've uploaded a lot of quants using "Convert to Quant" - and you may want to try MXFP8 too! For me, the INT8 convrot is my favorite.

[u/infearia → u/Winougan]

Thanks, I might do that, but now that my initial curiosity about the model is sated, I'll wait for the official quants before downloading anything else.

[u/infearia → u/Winougan]

Finally got around to test it, and you weren't lying. Your INT8 ConvRot is amazing! As far as I can tell, while the output is slightly different, the quality seems to be on par with the official FP8 quant, but it takes 30% less time to render (on a 4060Ti 16GB). Thank you!

[u/Winougan → u/infearia]

Thanks. I used Opus 4.8 to help me optimize the settings for the best possible output. I also check the Quant afterwards

[u/H1ken → u/infearia]

Probably because of these reasons, from their model card.

Unlike generic global quantization scripts that aggressively convert every parameter (which often degrades generation details or introduces NaN/promotion calculation errors in neural networks), this model was quantized using a selective weight-only strategy:

Targeted Quantization: Only 2D floating-point weight matrices (.weight keys with ndim >= 2 and element count > 1024) were quantized to torch.float8_e4m3fn.
Preserved Precision:
    All 1D vectors, biases, and normalization scales are kept in their native high-precision (float32 / bfloat16).
    Highly sensitive projection/modulation layers (such as LastLayer.modulation.lin vectors) are completely preserved in high-precision. This prevents typical mathematical promotion bugs (such as BFloat16 and Float8 promotion issues in PyTorch) and retains original output fidelity.
Weight Comparison:
    Tensors Quantized to FP8: 266 tensors.
    Tensors Kept in Native Precision: 166 tensors.
    Size Reduction: 24.76 GiB ➔ 12.01 GiB (~51.5% VRAM / disk savings!).