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# 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](../../modular_diffusers/overview). 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