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

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("path/to/krea2-diffusers", torch_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")

Krea2Pipeline

[[autodoc]] Krea2Pipeline

• all
• call

Krea2PipelineOutput

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