Krea 2 reference
Krea 2 (K2) is a flow-matching text-to-image model: a 12B dense DiT (grouped-query attention) with a Qwen3-VL text encoder (multi-layer feature aggregation) and the Qwen-Image VAE (AutoencoderKLQwenImage). Pipeline class: Krea2Pipeline. It ships as two checkpoints designed to work together:
| Repo | Role | Use it for |
|---|---|---|
krea/Krea-2-Turbo |
8-step distilled | Inference / demos |
krea/Krea-2-Raw |
base, non-distilled | LoRA training — not for inference |
For a LoRA Space, load Turbo. Krea 2 LoRAs are trained on RAW but run on Turbo (they "express strongly on Turbo"), and RAW is explicitly not meant for inference — expect poor quality if you load it directly. So a demo should almost always use krea/Krea-2-Turbo. The official LoRA cards confirm this ("To be used on krea/Krea-2-Turbo").
Needs a
diffusersbuild that includesKrea2Pipeline(both repos arelibrary_name: diffusers). Iffrom diffusers import Krea2Pipelinefails, the installeddiffuserspredates the integration — update it.
Required dependencies
diffuserswithKrea2Pipeline.transformersrecent enough for Qwen3-VL (the text encoder is aQwen3VLModel, e.g.Qwen/Qwen3-VL-4B-Instruct).torchvision— the Qwen3-VL processor pulls it in transitively; missing it is a startupImportError. Always include it.sentencepieceif you see tokenizer-related ImportErrors at startup.
Default load + LoRA (Turbo)
import torch
from diffusers import Krea2Pipeline
pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
pipe.transformer.load_lora_adapter("user/my-krea2-lora", weight_name="my_lora.safetensors")
pipe.transformer.set_adapters("default", weights=1.0)
# include the LoRA's trigger word(s) from its card
image = pipe(
"a deer grazing in a forest, <trigger words>",
num_inference_steps=8, guidance_scale=0.0,
generator=torch.Generator("cuda").manual_seed(0),
).images[0]Krea 2 LoRAs load through the transformer's adapter API (pipe.transformer.load_lora_adapter(...) + pipe.transformer.set_adapters("default", weights=1.0)), per the official LoRA cards — not the pipeline-level pipe.load_lora_weights. Honor the LoRA's trigger word(s) and recommended weight (1.0 by default).
Inference recipe
- Turbo (the demo default):
num_inference_steps=8,guidance_scale=0.0(guidance disabled), LoRA weight1.0. - RAW: training only — don't ship a RAW inference demo; its quality is intentionally low (it's the malleable base you fine-tune on, then run on Turbo).
guidance_scale convention (gotcha)
Krea 2 enables guidance whenever guidance_scale > 0 and computes velocity as cond + guidance_scale * (cond − uncond) (≡ the usual CFG formulation with scale 1 + guidance_scale). So Turbo disables guidance with guidance_scale=0.0 — not 1.0.
Resolution
height/width must be divisible by 16 (vae_scale_factor * patch_size); the pipeline rounds up to a multiple of 16 (with a warning) otherwise. Default 1024×1024.
ZeroGPU duration
Standard T2I: place modules at module scope, pipe.to("cuda"), no torch.compile. Turbo 8-step 1024² is fast (≈ 20–40s) — set @spaces.GPU(duration=...) comfortably above that. The 12B DiT + Qwen3-VL encoder fits ZeroGPU; if you OOM on the default size, try @spaces.GPU(size="xlarge").
Things to watch
- Load Turbo, not RAW, for demos. RAW is the training base and not meant for inference.
guidance_scale=0disables guidance (Krea convention, unlike pipelines where 1.0 is "off"). Turbo = 8 steps,guidance_scale=0.0.- LoRAs use
pipe.transformer.load_lora_adapter+set_adapters("default", …)(transformer-level), and have trigger words — read the card. - New pipeline → update diffusers if
from diffusers import Krea2Pipelinefails.