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Krea 2 LoRA Inference with Diffusers (AI Toolkit-trained)

API model id: krea2
URL slug: krea2
AI Toolkit training arch: krea2

This page documents the reference inference pipeline for krea2 — the full (non-distilled) Krea 2 text-to-image checkpoint. It is designed for running LoRAs trained with ostris/ai-toolkit while minimizing training preview vs inference mismatch. If you are trying to reproduce AI Toolkit sample previews, treat the code linked below as the source of truth.

Run in the cloud (optional): If you want to reproduce the examples on this page in a pinned runtime without local CUDA/driver setup (and reduce preview-vs-inference drift), run it via RunComfy’s Cloud AI Toolkit (Train + Inference). 👉 You can open it here: Cloud AI Toolkit (Train + Inference)

Quick facts

Field	Value
Pipeline	`src/pipelines/krea2.py`
Base checkpoint	`krea/Krea-2-Raw` (single file `raw.safetensors` at the repo root)
Text encoder	`Qwen/Qwen3-VL-4B-Instruct` (a stack of 12 hidden-state layers)
VAE	`Qwen/Qwen-Image` (`vae/` subfolder, f8, 16 latent channels)
Defaults	`sample_steps=30`, `guidance_scale=4.0`, `seed=42`
Resolution snapping	Floors width/height to a multiple of 16 (VAE f8 x patch 2)
Control image	No
LoRA scale behavior	Merged into the transformer at load. Changing `loras[].network_multiplier` triggers a full model reload.
Needs AI Toolkit	Yes — the transformer, packing and sampler come from `extensions_built_in/diffusion_models/krea2/`.
Gated weights	Yes — `krea/Krea-2-Raw` is HF-gated. `HF_TOKEN` must belong to an account that accepted the Krea 2 license.

Reference implementation (source of truth)

Pipeline implementation: src/pipelines/krea2.py
Shared behaviors (snapping, seeding, LoRA base logic): src/pipelines/base.py
Request schema (parameter names): src/schemas/request.py
Model ids (enum): src/schemas/models.py
Pipeline registry (model → class): src/pipelines/__init__.py
Upstream model + sampler: extensions_built_in/diffusion_models/krea2/ in ostris/ai-toolkit

Minimal API request

{
  "model": "krea2",
  "trigger_word": "sks",
  "prompts": [
    {
      "prompt": "[trigger] a photo of a person",
      "width": 1024,
      "height": 1024,
      "seed": 42,
      "sample_steps": 30,
      "guidance_scale": 4.0,
      "neg": ""
    }
  ],
  "loras": [
    {
      "path": "my_lora_job/my_lora.safetensors",
      "network_multiplier": 1.0
    }
  ]
}

Pipeline behavior that matters

guidance_scale is 0-normalized inside the model. AI Toolkit computes guidance = max(0, guidance_scale - 1) before sampling, and this pipeline does exactly the same, so a guidance_scale here means the same thing as sample.guidance_scale in a training config. Do not subtract the 1 yourself.
The flow-matching schedule shifts mu based on the image-token count (endpoints 256 → 0.5 and 6400 → 1.15). mu is deliberately not pinned to 1.15 for the distilled checkpoints, because no AI Toolkit krea2 preset pins it either — preview parity wins over the vendor default.
LoRA is merged directly into the transformer weights, so network_multiplier is fixed after load. A different scale forces a full reload (~26 GB checkpoint).
LoRA keys are read in AI Toolkit’s on-disk format (diffusion_model.*, peft-style lora_A/lora_B, no .alpha tensors — a missing alpha defaults to rank).

Preview-matching notes (training preview vs inference mismatch)

Start from 30 steps / guidance 4.0 — the AI Toolkit defaults for krea2.
Width/height are floored to a multiple of 16.
The model needs roughly 33 GiB of VRAM in bf16 (a ~12.8B transformer plus a 4B text encoder). This pipeline does not quantize, while AI Toolkit training defaults to float8; that is a known and deliberate parity delta.

What to compare when debugging mismatch

Confirm the effective width/height after snapping (the server floors to the divisor).
Match sample_steps, and remember guidance_scale is 0-normalized.
Match loras[].network_multiplier.
Confirm the LoRA was trained for krea2 (safetensors metadata ss_base_model_version is krea2).