u/y3kdhmbdb2ch2fc6vpm2

I have a problem. Recently my outputs in Krea 2 have blotches/banding/noise. This occurs on smooth surfaces and the edges of objects.

Images without reddit compression: img1, img2, img3

Things I tried today, but that didn't make any difference:

• Krea2 turbo int8 convrot / fp8 / bf16 / raw fp8
• Windows ComfyUI 0.31 / 0.30 / 0.26
• comfy-kitchen 0.2.30 / 0.2.27 / 0.2.10
• PyTorch 2.12 cu132 / 2.12 cu130 / 2.8 cu128
• Older NVIDIA driver

anyone else experienced this? any ideas?

test prompt: "a stylized 3D character render of a young woman, waist-up, neutral background, clean materials, and a contemporary high-quality character design presentation, dark environment, gray walls, low light"

I just created that lora from 63 Lady Dimitrescu images in the dataset

used OneTrainer on RTX 5070 Ti, 32 GB RAM and NVMe

trained in 1 MP (res 1024), offload 0.5, speed ~2.5 s/it, full training taken about 2.5-3h

I set timestep shift to 2.5 for res 1024 as suggested in this kohya md and I think it worked well

all samples generated with 2 MP

CivitAI -> https://civitai.com/models/2828952/lady-dimitrescu-krea2-lora

Full res comparisons without reddit compression -> img1, img2, img3, img4, img5

training Krea2 is so enjoyable!

I just trained new lora (after the pin-up one). It's inspired by iconic style of C. M. Duffy.

Dataset: 83 high quality images, trained in 1024 res

Used OneTrainer and RTX 5070 Ti 16 GB + 32 GB RAM

CivitAI -> https://civitai.com/models/2813229/c-m-duffy-style-krea2-lora

I recently created my first character LoRA (Ciri from Witcher 3) and now my first style LoRA ever, both for Krea2. It's amazing that these trainings are so easy.

Used OneTrainer on an RTX 5070 Ti 16 GB + 32 GB RAM

Details: rank/alpha 16, res 768, lr 0.0002, batch 1, acc steps 2, steps 1740, epochs 60, adamw, cosine, w8a8

Link to CivitAI -> https://civitai.com/models/2801306/gil-elvgren-pin-up-style-krea2-lora

I recommend to use strength 0.6-0.8 for more general pin-up style. Strength 1.0 is for all who love Gil Elvgren's work (e.g. me)

(it also can do some **** stuff when is used with refusal lora etc.)

This is my second LoRA ever

You guys liked the last LoRA with Ciri (and like comparisons just like me), so I trained another one, this time with Yennefer of Vengerberg. I'm still shocked how easy it is to teach Krea2 how the character in The Witcher 3 looks like, including small details

It can generate 3 variants of the Yennefer: default outfit, lingerie outfit, unclothed. Every variant has own special trigger word

Technical info:

• dataset: 76 high res screenshots from The Witcher 3 4.04 (ultra+, RT on), various poses, mimics, environments etc, post cropped manually
• hardware: RTX 5070 Ti 16 GB + 32 GB RAM + NVMe
• software: OneTrainer
• settings: trained on Krea 2 RAW INT8 W8A8, 32 rank, res 768, 30 epochs, 2280 steps, adamw8bit, lr 0.0001
• notes: all samples generated with Krea2 Turbo INT8 Convrot + mrsh_y3nn3f3r lora at 0.8-1.0 strength, euler/simple, 10 steps, cfg 1.0

CivitAI link -> https://civitai.com/models/2791376/yennefer-from-the-witcher-3-krea2-lora

It has also **** capabilities, if you want check it, change .com in the url to .red

HF! Any suggestions for the next character?

I just created my very first lora, and I'm shocked at how easy it was to teach Krea2 what Ciri in The Witcher 3 looks like.

Technical info:

• dataset: 45 screenshots from The Witcher 3 4.04 (RT off), post cropped manually
• hardware: RTX 5070 Ti 16 GB + 32 GB RAM + NVMe
• software: Fizgig 2.7.0
• settings: trained on Krea 2 RAW BF16, 32 rank, 1 MP (1024x1024), 40 epochs, 1800 steps, bucketing on, optimizer/LR: Fizgig adaptive LR, 1e-4 start, 5e-5 min
• notes: training time 2-3h, all samples generated with Krea2 Turbo INT8 Convrot + ciri lora at 0.6-1.0 strength

CivitAI link -> https://civitai.com/models/2784239/ciri-from-the-witcher-3-krea2-lora

I'd appreciate it if you could test it out. This is my first LoRA ever, and I'm sure there's room for improvement.

I used Fizgig, but I see the ai-toolkit and onetrainer are often mentioned here. Which one is currently the best for my hardware and Krea2?

In my last comparison, you suggested checking quantizations with LoRAs, so I generated 126 images, also using the new INT4_convrot model.

comparisons full res: comp1, comp2, comp3, comp4, comp5, comp6, comp7

some of the images full res: img1, img2, img3, img4, img5, img6

one comparison set = one prompt and seed
first row = no lora
second row = single lora
third row = two loras

(first gen -> second gen)

• BF16: 22.00 s -> 13.36 s
• GGUF_Q8: 41.46 s -> 35.82 s
• INT8_convrot: 8.13 s -> 5.88 s
• FP8_scaled: 10.77 s -> 9.35 s
• NVFP4: 9.33 s -> 7.78 s
• INT4_convrot: 7.15 s -> 5.84 s

Using one or multiple loras did not change the generation times on any of the models.

Details:

• RTX 5070 Ti 16 GB + 32 GB DDR5 + NVMe
• ComfyUI: 0.27.0, Python: 3.13.12, PyTorch: 2.12.0+cu130, default pytorch attention.
• 1024x1024, euler / simple, 8 steps, cfg 1.0, wan 2.1 fp32 vae, qwen 3vl 4b bf16 clip

What do you think? What should I compare next?

I like comparisons (as you can see here and here), so I generated 120 images (20 comparison sets) comparing all the turbo models available in the official ComfyUI Krea2 repository (and GGUF).

(first gen -> second gen -> queued gen)
BF16:
19.44 s -> 13.13 s -> 12.60 s
GGUF_Q8:
22.81 s -> 24.22 s -> 14.28 s
INT8_convrot:
10.26 s ->  6.16 s ->  5.87 s
MXFP8:
13.46 s ->  9.47 s ->  9.33 s
FP8_scaled:
13.76 s ->  9.16 s ->  9.17 s
NVFP4:
9.47 s ->  7.78 s ->  8.30 s
Comparison set (6 imgs) generation time: 71 s

Details:

• RTX 5070 Ti 16 GB + 32 GB DDR5 + NVMe
• ComfyUI: 0.27.0, Python: 3.13.12, PyTorch: 2.12.0+cu130, default pytorch attention.
• 1024x1024, euler / simple, 8 steps, cfg 1.0, wan 2.1 fp32 vae, qwen 3vl 4b bf16 clip, no loras, 1 set = 1 seed

Full res: img1, img2, img3, img4, img5, img6, img7, img8, img9, img10, img11, img12, img13, img14, img15, img16, img17, img18, img19, img20

Which one is the best overall? Which one is closest to BF16? And is BF16 always the best? GL & HF

Edit:
There is a typo on the images, it's INT8_convrot, not invrot ofc.

I made an 80-image comparison to evaluate the output diversity and prompt adherence of the Raw + Turbo LoRA vs the Turbo model.

Full res image: https://i.imghippo.com/files/MsiC4585KE.webp

Left:
Krea2 Turbo INT8 convrot, euler/simple, CFG 1.0, 8 steps, ~7s

Right:
Krea2 RAW INT8 convrot + Krea2 Turbo LoRA @ 0.6, euler/simple, CFG 1.5, 16 steps, ~22s

No loras (except the Turbo), no bypass filters, no bypass nodes, no seed randomizers. Plain Krea2

My thoughts: the Turbo model is faster and often produces detailed images, but firstly, they are very repetitive across different seeds, and secondly, they usually have a very generic composition (the main object symmetrically centred, etc., more "AI" look). The final choice depends on the individual’s priorities - whether you prefer generation speed or greater variety

I'm not an expert on T2I (I usually do I2I in Klein 9B), but generating with Krea2 is actually quite fun so I decided to (first time) share it with you. These aren't intended to be 1:1 recreations of the game, It's more like model test just inspired by the Gothic.

I used:

• Krea2 RAW INT8 convrot + Krea2 Turbo LoRA at 0.6 strength + clip Qwen BF16
• Krea2FilterBypass 2 vector LoRA
• ComfyUI-Krea2T-Enhancer node

Another info:

• RTX 5070 TI 16 GB + 32 GB DDR5
• euler simple, 12 steps, PiD
• single generation with WAN FP32 VAE takes 10.2s (1024x1024 output)
• single generation with Nvidia PiD (Gemma BF16) takes 44s (4096x4096 output)
• ComfyUI 0.27.0, Driver: 610.62, OS: Windows 11, PyTorch: 2.12.0+cu130
• default PyTorch attention (no flash, no sage)

I put together a benchmark of the new INT8 ConvRot model on my RTX 5070 Ti.

Blue = FP8 Scaled
Green = INT8 Convrot

The workflow uses the native loader in ComfyUI 0.27.0, not the custom node.

Default PyTorch attention, Driver: 610.62, OS: Windows 11, ComfyUI: 0.27.0, Python: 3.13.12, PyTorch: 2.12.0+cu130, CUDA: 13.0

The speed improvement is huge. The output is slightly different, maybe even better, need more testing.

Krea2 INT8 ConvRot: https://huggingface.co/Comfy-Org/Krea-2/tree/main/diffusion_models