vllm-omni/recipes/krea/Krea-2.md

Krea 2 for text-to-image (base + LoRA)

Summary

• Vendor: Krea
• Model: krea/Krea-2-Turbo (few-step distilled) and krea/Krea-2-Raw
• Task: Text-to-image (T2I)
• Mode: Offline engine and online OpenAI-compatible serving
• Maintainer: Community

When to use this recipe

Use this recipe for a known-good starting point to run the Krea 2 single-stream MMDiT with vLLM-Omni on a single 80 GB NVIDIA H100 — both the offline text_to_image.py entrypoint and the online /v1/images/generations route, including PEFT LoRA adapters. Krea2Pipeline is auto-detected from model_index.json; guidance is checkpoint-aware (distilled Turbo runs no-CFG, Raw runs CFG).

References

• Related example under examples/: examples/offline_inference/text_to_image/README.md (see the "Krea 2" section)
• Feature support: docs/user_guide/diffusion_features.md
• Related PR: #4730

Hardware Support

This recipe documents a single-GPU CUDA layout on H100 80 GB. Add more platforms (ROCm / NPU) or multi-GPU layouts as community validation lands.

GPU

1× H100 80GB

Environment

Versions from a working editable install (activate vllm-omni/.venv or your equivalent):

• OS: Linux
• Python: 3.12
• Driver / runtime: NVIDIA driver 595.71.05 (CUDA 13.x)
• torch: 2.11.0+cu130
• vLLM: 0.24.0
• vLLM-Omni: editable install from this repo (Git 960ce8f6)
• Transformers: 5.12.1

Krea 2 weights are ~30 GB and fit comfortably on a single 80 GB H100.

Command

Offline — few-step distilled (Turbo) checkpoint (2048×2048, no CFG):

python examples/offline_inference/text_to_image/text_to_image.py \
  --model krea/Krea-2-Turbo \
  --prompt "a fox in the snow" \
  --num-inference-steps 8 \
  --guidance-scale 0.0 \
  --height 2048 --width 2048 \
  --output krea2_turbo.png

Offline — Raw checkpoint (1024×1024, CFG enabled):

python examples/offline_inference/text_to_image/text_to_image.py \
  --model krea/Krea-2-Raw \
  --prompt "a fox in the snow" \
  --num-inference-steps 28 \
  --guidance-scale 4.5 \
  --height 1024 --width 1024 \
  --output krea2_raw.png

LoRA (offline) — pass a PEFT adapter with --lora-path / --lora-scale. NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA is a vLLM-Omni-compatible repackaging of krea/Krea-2-LoRA-darkbrush; its trigger word is monochrome ink wash style:

python examples/offline_inference/text_to_image/text_to_image.py \
  --model krea/Krea-2-Turbo \
  --prompt "a fox in the snow, monochrome ink wash style" \
  --num-inference-steps 8 --guidance-scale 0.0 \
  --lora-path NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA --lora-scale 1.0 \
  --output krea2_darkbrush.png

Online — serve and query the OpenAI-compatible image route:

vllm serve krea/Krea-2-Turbo --omni --enforce-eager --port 8091

curl -s http://localhost:8091/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "a fox in the snow, monochrome ink wash style",
    "size": "1024x1024",
    "num_inference_steps": 8,
    "guidance_scale": 0.0,
    "seed": 42,
    "lora": {"name": "darkbrush", "path": "NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA", "scale": 1.0}
  }' | jq -r '.data[0].b64_json' | base64 -d > krea2_online.png

Cache-DiT acceleration (offline) — add --cache-backend cache_dit:

python examples/offline_inference/text_to_image/text_to_image.py \
  --model krea/Krea-2-Turbo --prompt "a fox in the snow" \
  --num-inference-steps 8 --guidance-scale 0.0 \
  --cache-backend cache_dit --output krea2_cachedit.png

Verification

# Offline: the script prints "Saved generated image to <path>"; confirm the file exists.
ls -lh krea2_turbo.png

For the online route, decode the returned b64_json (see the curl above) and confirm a valid 1024×1024 PNG is written.

Notes

• Key flags: distilled Turbo → few steps (~8) with guidance_scale=0.0; Raw → more steps (~28) with guidance_scale>0 (CFG). guidance-scale follows the Krea 2 convention velocity = cond + guidance_scale * (cond - uncond).
• LoRA: adapters must be PEFT format (adapter_config.json + adapter_model.safetensors); the projections are ReplicatedLinear (264 modules). Offline uses --lora-path (which sets OmniDiffusionSamplingParams.lora_request / lora_scale); online uses the top-level lora object. Style LoRAs like darkbrush need their trigger word in the prompt.
• Cache-DiT is supported; has_separate_cfg is checkpoint-aware (False for Turbo no-CFG, True for Raw CFG).
• Known limitations: TP / SP / CFG-Parallel are not yet wired for Krea 2. HSDP, layerwise CPU offload, and VAE-patch-parallel (decode) are supported.

XPU

1x Intel Arc Pro B70 (32 GB)

Offline text-to-image for the distilled Turbo checkpoint at 1024x1024, with layerwise offload streaming the transformer blocks.

Environment

• OS: Linux
• Python: 3.10+
• torch: 2.13.0+xpu
• vLLM: 0.29.0 (98dff2a8)
• vLLM-Omni: main at 4c7a98c2

Command

python examples/offline_inference/text_to_image/text_to_image.py \
  --model krea/Krea-2-Turbo \
  --prompt "a photo of a sunset over mountains" \
  --height 1024 --width 1024 \
  --num-inference-steps 8 \
  --enable-layerwise-offload \
  --vae-use-tiling \
  --enforce-eager \
  --output krea2_turbo_output.png

Given spare cards, --enable-cpu-offload replaces layerwise streaming. At 8 steps the swap dominates the run, and cards past the second shard nothing further.

  --tensor-parallel-size 2 \
  --enable-cpu-offload

Offload	Cards	Peak VRAM	Time
--enable-layerwise-offload	1	12.3 GiB	27 s
--enable-cpu-offload	2	25.6 GiB	59 s
--enable-cpu-offload	4	25.6 GiB	59 s

Verification

Confirm krea2_turbo_output.png is written as a 1024x1024 PNG matching the prompt.

Notes

• Memory usage: 7.8 GiB loaded, 12.3 GiB peak, about 26 s per image.
• Known limitations: only offline Turbo generation was qualified. Krea-2-Raw, LoRA, Cache-DiT, and online serving are out of scope for this profile.