Krea 2

Krea 2 (K2) is a flow-matching text-to-image model built around a single-stream MMDiT with grouped-query attention. A Qwen3-VL text encoder provides the conditioning: instead of the last hidden state, hidden states from twelve decoder layers are tapped per token and fused inside the transformer by a small text-fusion stage. Images are decoded with the Qwen-Image VAE.

Two checkpoints are released, sharing the same architecture but with different recommended sampler settings:

Base (midtrain) — use the full sampler with classifier-free guidance: num_inference_steps=28, guidance_scale=4.5.
TDM (distilled) — distilled for few-step sampling, run with num_inference_steps=8 and guidance disabled (guidance_scale=0.0).

guidance_scale follows the Krea 2 convention: the velocity is computed as cond + guidance_scale * (cond - uncond) and guidance is enabled whenever guidance_scale > 0 (this equals the usual CFG formulation with scale 1 + guidance_scale).

Text-to-image

python

import torch
from diffusers import Krea2Pipeline

# Load from a local directory produced by the Krea 2 conversion (no hub repo yet).
pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "a fox in the snow"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    num_inference_steps=28,
    guidance_scale=4.5,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
image.save("krea2.png")

We additionally provide an example for using Krea2 Turbo :

python

import torch
from diffusers import Krea2Pipeline

pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", dtype=torch.bfloat16)
pipe.to("cuda")

image = pipe(
    "a fox in the snow",
    height=1024,
    width=1024,
    num_inference_steps=8,
    guidance_scale=0.0,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
image.save("krea2_turbo.png")

Krea2Pipeline

[[autodoc]] Krea2Pipeline

all
call

Krea2PipelineOutput

[[autodoc]] pipelines.krea2.pipeline_output.Krea2PipelineOutput

Modular

Krea 2 is also available as a modular pipeline. Classifier-free guidance is configured through the guider component rather than a guidance_scale call argument. Krea 2 uses cond-anchored CFG, which is [ClassifierFreeGuidance] with use_original_formulation=True.

python

import torch
from diffusers import ClassifierFreeGuidance, ModularPipeline

pipe = ModularPipeline.from_pretrained("krea/Krea-2-Raw")
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")


image = pipe(
    prompt="a fox in the snow",
    height=1024,
    width=1024,
    num_inference_steps=28,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
image.save("krea2.png")

We additionally provide an example for using Krea2 Turbo. The distilled checkpoint maps to its own set of blocks ([Krea2TurboAutoBlocks]): it runs guidance-free (no guider), takes no negative prompt, and samples in a few steps. ModularPipeline.from_pretrained picks the turbo blocks automatically from the checkpoint's is_distilled config, so no guidance configuration is needed:

python

import torch
from diffusers import ModularPipeline

pipe = ModularPipeline.from_pretrained("krea/Krea-2-Turbo")
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")

image = pipe(
    prompt="a fox in the snow",
    height=1024,
    width=1024,
    num_inference_steps=8,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
image.save("krea2_turbo.png")

Krea2ModularPipeline

[[autodoc]] Krea2ModularPipeline

Krea2AutoBlocks

[[autodoc]] Krea2AutoBlocks

Krea2TurboModularPipeline

[[autodoc]] Krea2TurboModularPipeline

Krea2TurboAutoBlocks

[[autodoc]] Krea2TurboAutoBlocks