---
title: Krea-2
metatags:
    description: "Deploy Krea-2 with SGLang - fast, high-quality text-to-image generation."
---

## 1. Model Introduction

[Krea-2](https://huggingface.co/krea/Krea-2-Turbo) is a high-quality text-to-image diffusion model from [Krea](https://www.krea.ai/). It ships in two variants that share the same backbone and differ only in their sampling recipe:

- **[Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo)** - a distilled, few-step model that produces photorealistic images in only **8 inference steps** with no classifier-free guidance (`guidance_scale = 1.0`), ideal for fast and interactive generation.
- **[Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw)** - the base (non-distilled) model that trades speed for maximum fidelity, using a longer schedule (~52 steps) with classifier-free guidance (`guidance_scale ≈ 4.5`).

Both variants are built on a single-stream MMDiT with a Qwen3-VL text encoder and the Qwen-Image VAE, and are distributed in the standard diffusers layout (a `model_index.json` plus sharded `transformer/`, `text_encoder/`, `vae/`, `tokenizer/`, and `scheduler/` folders). SGLang loads them **natively** - just point `--model-path` at the repo, no conversion step required.

**Key Features:**

- **Two variants, one pipeline**: switch between fast (Turbo) and high-fidelity (Raw) by changing only the model path and the sampling settings.
- **Photorealistic generation** at 1024x1024 and other resolutions.
- **Native diffusers loading**: components (DiT, text encoder, VAE, scheduler) are read straight from the repo's `model_index.json`.

For more details, see the [Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo) and [Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw) HuggingFace pages.

## 2. SGLang-diffusion Installation

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the [official SGLang-diffusion installation guide](https://docs.sglang.io/docs/sglang-diffusion/installation) for installation instructions.

## 3. Model Deployment

This section covers deploying Krea-2-Turbo for fast, high-quality image generation.

### 3.1 Basic Configuration

Krea-2-Turbo generates high-quality images in only 8 inference steps. Launch the server with:

```bash Command
sglang serve \
  --model-path krea/Krea-2-Turbo \
  --num-gpus 1 \
  --port 30000
```

The step count and guidance scale are **request-time** settings (see [API Usage](#4-api-usage)); Krea-2-Turbo defaults to 8 steps with `guidance_scale = 1.0`.

### 3.2 Configuration Tips

Currently supported optimizations are listed [here](/docs/sglang-diffusion/compatibility_matrix).

- `--num-gpus`: Number of GPUs to use.
- Multi-GPU (tensor and/or sequence parallelism): see [Section 3.3](#3-3-multi-gpu-tensor-and-sequence-parallelism).

### 3.3 Multi-GPU: tensor and sequence parallelism

Krea-2 supports two multi-GPU axes that can be combined; `--num-gpus` must equal
`tp_size × ulysses_degree`.

- **Tensor parallelism (`--tp-size N`)** shards the DiT weights across GPUs, lowering
  per-GPU VRAM. Krea-2's attention heads (48 query / 12 KV) and text heads (20) are
  divisible by a tp size of 1, 2, or 4.
- **Sequence parallelism / Ulysses (`--ulysses-degree N`)** shards the image-token
  sequence across GPUs while keeping the text prefix replicated. It does **not** shard
  weights (per-GPU VRAM is unchanged), but its output is **bitwise-identical** to
  single-GPU. It currently requires a single prompt per request (ragged/padded
  multi-prompt batches under SP are not supported — use `--tp-size` for those).

```bash Command
# Tensor parallel (2 GPUs) — lowest per-GPU VRAM (DiT weights sharded)
sglang serve --model-path krea/Krea-2-Turbo --num-gpus 2 --tp-size 2 --port 30000

# Sequence parallel / Ulysses (2 GPUs) — output bitwise-identical to single-GPU
sglang serve --model-path krea/Krea-2-Turbo --num-gpus 2 --ulysses-degree 2 --port 30000

# Hybrid TP × SP (4 GPUs) — composes both axes
sglang serve --model-path krea/Krea-2-Turbo --num-gpus 4 --tp-size 2 --ulysses-degree 2 --port 30000
```

Measured on 2× H200 (Krea-2-Turbo, 8 steps, 1024×1024): `--tp-size 2` and
`--ulysses-degree 2` each give ~1.7× denoise speedup over single-GPU; the hybrid
TP=2 × SP=2 reaches ~2.8× on 4 GPUs. **Choosing:** on memory-constrained GPUs prefer
`--tp-size` (it shards the ~24 GB DiT, e.g. ~38 GB → ~27 GB per GPU on 2 GPUs); on
large-VRAM GPUs sequence parallelism is marginally faster and numerically exact, and
the two compose for the highest throughput.

## 4. API Usage

For complete API documentation, please refer to the [official API usage guide](/docs/sglang-diffusion/api/openai_api).

### 4.1 Generate an Image

Generate an image with the OpenAI-compatible images API:

```python Example
import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:30000/v1")

response = client.images.generate(
    model="krea/Krea-2-Turbo",
    prompt="a red fox sitting in fresh snow, golden hour, photorealistic",
    n=1,
    response_format="b64_json",
)

# Save the generated image
image_bytes = base64.b64decode(response.data[0].b64_json)
with open("output.png", "wb") as f:
    f.write(image_bytes)
```

You can also generate a single image from the command line:

```bash Command
sglang generate --model-path krea/Krea-2-Turbo \
    --prompt "a red fox sitting in fresh snow, golden hour, photorealistic" \
    --num-inference-steps 8 --height 1024 --width 1024 --save-output
```

### 4.2 Advanced Usage

#### 4.2.1 Cache-DiT Acceleration

SGLang integrates [Cache-DiT](https://github.com/vipshop/cache-dit), a caching acceleration engine for Diffusion Transformers (DiT), to speed up inference with minimal quality loss. Enable it by setting `SGLANG_CACHE_DIT_ENABLED=true`. For more details, see the SGLang Cache-DiT [documentation](/docs/sglang-diffusion/cache_dit).

Cache-DiT works for **both** Krea-2 variants with no extra configuration: SGLang tracks each request's classifier-free-guidance mode, so Krea-2-Turbo (no CFG, `guidance_scale = 1.0`) and Krea-2-Raw (CFG, `guidance_scale ≈ 4.5`) both cache correctly and automatically.

**Basic Usage**

```bash Command
SGLANG_CACHE_DIT_ENABLED=true sglang serve \
  --model-path krea/Krea-2-Turbo \
  --num-gpus 1 \
  --port 30000
```

Measured per-image denoise speedup with the default cache settings (NVIDIA H200, 1024x1024, seed 0):

| Variant | Inference steps | Denoise (no cache → cache) | Speedup |
| :--- | :--- | :--- | :--- |
| Krea-2-Turbo (no CFG) | 8 | 1.27s → 0.92s | ~1.4x |
| Krea-2-Raw (CFG 4.5) | 50 | 18.0s → 6.3s | ~2.9x |

Caching has the most headroom on Raw's longer schedule; the 8-step distilled Turbo has only a few cacheable steps after warmup.

**Advanced Usage**

- DBCache Parameters: DBCache controls block-level caching behavior:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Fn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_FN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of first blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Bn</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_BN`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Number of last blocks to always compute</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>W</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_WARMUP`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>4</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Warmup steps before caching starts</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>R</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_RDT`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>0.24</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Residual difference threshold</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MC</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_MC`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>3</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Maximum continuous cached steps</td>
    </tr>
  </tbody>
</table>
- TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion (best suited to the longer Raw schedule; not recommended for the 8-step Turbo):

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
    <col style={{width: "25.0%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Env Variable</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Default</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Enable</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TAYLORSEER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>false</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Enable TaylorSeer calibrator</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Order</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`SGLANG_CACHE_DIT_TS_ORDER`</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Taylor expansion order (1 or 2)</td>
    </tr>
  </tbody>
</table>

  Combined Configuration Example (Krea-2-Raw, default cache settings shown explicitly):

```bash Command
SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=1 \
SGLANG_CACHE_DIT_BN=0 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.24 \
SGLANG_CACHE_DIT_MC=3 \
sglang serve --model-path krea/Krea-2-Raw
```

#### 4.2.2 Memory & CPU Offload

Krea-2's DiT is ~24 GB in bf16 (the bulk of the model). On memory-constrained GPUs you can keep less of it resident:

- `--dit-layerwise-offload`: stream the DiT's transformer blocks layer-by-layer with async host-to-device prefetch overlap, so only a small working set stays on the GPU. This is the primary way to fit Krea-2 on a single consumer / 32 GB-class card, at a modest latency cost. Tune the memory/latency trade-off with `--dit-offload-prefetch-size` (`0.0` prefetches one layer for the lowest memory; larger values prefetch more layers -- faster but more memory).
- `--dit-cpu-offload`: keep the whole DiT in host memory. Combine it with `--dit-layerwise-offload` for the lowest peak GPU memory (weights stay on host and only the layers needed for the current step are brought on-device).
- `--text-encoder-cpu-offload`: offload the Qwen3-VL text encoder (it is idle during the denoise loop).
- `--vae-cpu-offload`: offload the VAE.
- `--pin-cpu-memory`: pin host memory for offload. Add only as a temporary workaround if you hit `CUDA error: invalid argument`.

On large-VRAM GPUs (e.g. H200), keep everything resident (offloads off) for the fastest latency.

## 5. Benchmark

Test Environment:

- Hardware: NVIDIA H200 GPU (1x)
- Model: krea/Krea-2-Turbo (8 inference steps)
- sglang diffusion version: 0.5.13

**Server Command** (used for both benchmarks below):

```shell Command
sglang serve --model-path krea/Krea-2-Turbo --port 30000
```

### 5.1 Generate an image

**Benchmark Command**:

```shell Command
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --model krea/Krea-2-Turbo --dataset vbench --task text-to-image \
    --num-prompts 1 --max-concurrency 1
```

**Result**:

```text Output
================= Serving Benchmark Result =================
Task:                                    text-to-image
Model:                                   krea/Krea-2-Turbo
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  1.56
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.64
Latency Mean (s):                        1.5600
Latency Median (s):                      1.5600
Latency P99 (s):                         1.5600
--------------------------------------------------
Peak Memory Max (MB):                    37466.00
Peak Memory Mean (MB):                   37466.00
Peak Memory Median (MB):                 37466.00
============================================================
```

### 5.2 Generate images with high concurrency

**Benchmark Command**:

```shell Command
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --model krea/Krea-2-Turbo --dataset vbench --task text-to-image \
    --num-prompts 20 --max-concurrency 20
```

**Result**:

```text Output
================= Serving Benchmark Result =================
Task:                                    text-to-image
Model:                                   krea/Krea-2-Turbo
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  31.47
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     20/20
--------------------------------------------------
Request throughput (req/s):              0.64
Latency Mean (s):                        16.5000
Latency Median (s):                      16.5200
Latency P99 (s):                         31.1300
--------------------------------------------------
Peak Memory Max (MB):                    37468.00
Peak Memory Mean (MB):                   37466.40
Peak Memory Median (MB):                 37466.00
============================================================
```
