Model reference · open weights
Krea-2-sdnq-hadamard-uint4 is an open-weight image model from vladmandic. Krea-2-Turbo-sdnq-hadamard-uint4 (INT4) weighs 11.3 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.
What it is
| Released by | vladmandic |
|---|---|
| Type | Image models |
| Task | Text→image |
| Parameters (lead) | 7.0B |
| Runs with | diffusers |
| Released | 2026-07-03 |
| Popularity | 1k downloads / month |
| Weights | 11.3 GB (Krea-2-Turbo-sdnq-hadamard-uint4 (INT4), file size) |
| Licence | Licence not stated |
What it runs on
Weights 11.3 GB (file size) · its biggest part 7.7 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.
| Card | One 1024×1024 image | Counted memory |
|---|---|---|
| RTX 3060 12 GB | does not fit | 11.6 GB |
| RTX 4060 Ti 16 GB | tight (encoders offloaded) | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.
Running it yourself
Rent a machine by the hour. Runs as it is with diffusers — on the machine, in Python.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("vladmandic/Krea-2-Turbo-sdnq-hadamard-uint4", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")